Patent Publication Number: US-2017371544-A1

Title: Electronic system with learning mechanism and method of operation thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application is a Continuation of co-pending U.S. patent application Ser. No. 14/877,421 filed Oct. 7, 2015, and the subject matter thereof is incorporated herein by reference thereto. U.S. patent application Ser. No. 14/877,421 filed Oct. 7, 2015 further claims the benefit of U.S. Provisional Patent Application Ser. No. 62/099,067 filed Dec. 31, 2014, and the subject matter thereof is incorporated herein by reference thereto. 
    
    
     TECHNICAL FIELD 
     An embodiment of the present invention relates generally to an electronic system, and more particularly to a system with machine learning. 
     BACKGROUND 
     Modern consumer and enterprise electronics, especially devices such as graphical display systems, televisions, projectors, cellular phones, portable digital assistants, client workstations, data center servers, and combination devices, are providing increasing levels of functionality to support modern life. Research and development in the existing technologies can take a myriad of different directions. 
     The increasing levels of functionality typically require increasing memory and storage processing. Processor and memory capacity and bandwidth can be key factors in increasing device or system performance and functionality. As with other electronic components or modules, area and cost of memory are traded off with performance and functionality. 
     Processing large amounts of data can improve device or system performance and functionality. Unfortunately processing large amounts of data can consume a large amount of system bandwidth, introduce system access conflicts, and consume system resources, all of which reduce the system performance and functionality. 
     Thus, a need still remains for an electronic system with learning mechanism for processing large amounts of data to improve system performance. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations and the diminishing opportunities for meaningful product differentiation in the marketplace, it is increasingly critical that answers be found to these problems. Additionally, the need to reduce costs, improve efficiencies and performance, and meet competitive pressures adds an even greater urgency to the critical necessity for finding answers to these problems. 
     Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art. 
     SUMMARY 
     An embodiment of the present invention provides an electronic system including: a storage interface configured to receive system information; a storage control unit, coupled to the storage interface, configured to implement a preprocessing block for partitioning data based on the system information; and a learning block for processing partial data of the data for distributing machine learning processes. 
     An embodiment of the present invention provides a method of operation of an electronic system including: receiving system information with a storage interface; partitioning data, with a storage control unit configured to implement a preprocessing block, based on the system information; and distributing machine learning for processing partial data of the data, with the storage control unit configured to implement a learning block. 
     An embodiment of the present invention provides a non-transitory computer readable medium including stored thereon instructions to be executed by a control unit including: receiving system information with a storage interface; partitioning data, with a storage control unit configured to implement a preprocessing block, based on the system information; and distributing machine learning processes for processing partial data of the data, with the storage control unit configured to implement a learning block coupled to a storage block with the data the partial data. 
     Certain embodiments of the invention have other steps or elements in addition to or in place of those mentioned above. The steps or elements will become apparent to those skilled in the art from a reading of the following detailed description when taken with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an electronic system in an embodiment of the invention. 
         FIG. 2  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 3  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 4  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 5  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 6  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 7  is a block diagram of a portion of a storage device of the electronic system in an embodiment of the invention. 
         FIG. 8  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 9  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 10  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 11  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 12  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 13  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 14  is a process flow of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 15  is a block diagram of a portion of an electronic learning system of the electronic system in an embodiment of the invention. 
         FIG. 16  is examples of embodiments of the electronic system. 
         FIG. 17  is a flow chart of a method of operation of the electronic system in an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     In an embodiment of the invention, learning systems can include machine learning that can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can include classification, regression, feature learning, online learning, unsupervised learning, supervised learning, clustering, dimensionality reduction, structured prediction, anomaly detection, neural nets, or combination thereof. 
     In an embodiment of the invention, a learning system can include machine learning systems that can process or analyze “big data”. Parallel or distributed storage devices with in storage computing (ISC) can accelerate big data machine learning and analytics. This parallel or distributed learning system can offload functions to ISC for additional bandwidth and reduce input and output (I/O) for the storage and host processor. This parallel or distributed learning system can provide machine learning with ISC. 
     In an embodiment of the invention, a parallel or distributed learning system can be implemented with in storage computing (ISC), a scheduler, or combination thereof. ISC can provide significant improvements in the learning system including parallel or distributed learning. ISC can provide another processor for machine learning, an accelerator for assisting a host central processing unit, or combination thereof, such as preprocessing at an ISC to relieve a bandwidth bottleneck once detected. The scheduler can intelligently assign data, tasks, functions, operations, or combination thereof. 
     The following embodiments are described in sufficient detail to enable those skilled in the art to make and use the invention. It is to be understood that other embodiments would be evident based on the present disclosure, and that system, process, or mechanical changes may be made without departing from the scope of an embodiment of the present invention. 
     In the following description, numerous specific details are given to provide a thorough understanding of the invention. However, it will be apparent that the invention may be practiced without these specific details. In order to avoid obscuring an embodiment of the present invention, some well-known circuits, system configurations, and process steps are not disclosed in detail. 
     The drawings showing embodiments of the system are semi-diagrammatic, and not to scale and, particularly, some of the dimensions are for the clarity of presentation and are shown exaggerated in the drawing figures. Similarly, although the views in the drawings for ease of description generally show similar orientations, this depiction in the figures is arbitrary for the most part. Generally, the invention can be operated in any orientation. The embodiments have been numbered first embodiment, second embodiment, etc. as a matter of descriptive convenience and are not intended to have any other significance or provide limitations for an embodiment of the present invention. 
     Referring now to  FIG. 1 , therein is shown an electronic system  100  in an embodiment of the invention. The electronic system  100  with learning mechanism includes a first device  102 , such as a client or a server, a communication path  104 , such as a wireless or wired network, or combination thereof. The first device  102  can couple with a second device  106 , such as a client or server. The first device  102  can couple with the communication path  104  to couple with the second device  106 . For example, the first device  102 , the second device  106 , or combination thereof, can be of any of a variety of devices, such as a client, a server, display device, a node of a cluster, a node of super computers, a cellular phone, personal digital assistant, a notebook computer, other multi-functional device, or combination thereof. The first device  102  can couple, either directly or indirectly, to the communication path  104  to communicate with the second device  106  or can be a stand-alone device. 
     For illustrative purposes, the electronic system  100  is shown with the second device  106  and the first device  102  as end points of the communication path  104 , although it is understood that the electronic system  100  can have a different partition between the first device  102 , the second device  106 , and the communication path  104 . For example, the first device  102 , the second device  106 , or a combination thereof can also function as part of the communication path. 
     In an embodiment, the communication path  104  can span and represent a variety of networks. For example, the communication path  104  can include system bus, wireless communication, wired communication, optical, ultrasonic, or the combination thereof. Peripheral Component Interconnect Express (PCIe), Peripheral Component Interconnect (PCI), Industry Standard Architecture (ISA), Serial Advanced Technology Attachment (SATA), Small Computer Serial Interface (SCSI), Enhanced Integrated Drive Electronics (EIDE), Non-Volatile Memory host controller interface, Non-Volatile Memory express (NVMe) interface, Serial Attached Advanced Technology Attachment express (SATAe), and accelerated graphics port (AGP), are examples of system bus technologies. Satellite, cellular, Bluetooth, and wireless fidelity (WiFi), are examples of wireless communication. Ethernet, 10 Gigabit Ethernet, 40 Gigabit Ethernet, 100 Gigabit Ethernet, InfiniBand™, digital subscriber line (DSL), and fiber to the home (FTTH), are examples of wired communication. All of the aforementioned can be included in the communication path  104 . 
     In an embodiment, the first device  102  can include a first control unit  112 , a first storage unit  114 , a first communication unit  116 , and a first user interface  118 . The first control unit  112  can include a first control interface  122 . The first control unit  112  can execute a first software of a first storage media  126  to provide the intelligence of the electronic system  100 . The first control unit  112  can be implemented in a number of different manners. 
     For example, the first control unit  112  can be a processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a system on a chip (SOC), an embedded processor, a microprocessor, multi-processors, chip-multiprocessor(s) (CMP), a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. The first control interface  122  can be used for communication between the first control unit  112  and other functional units in the first device  102 . The first control interface  122  can also be used for communication that is external to the first device  102 . 
     In an embodiment, the first control interface  122  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . The first control interface  122  can be implemented in different ways and can include different implementations depending on which functional units or external units are being interfaced with the first control interface  122 . For example, the first control interface  122  can be implemented with a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), optical circuitry, waveguides, wireless circuitry, wireline circuitry, or a combination thereof. 
     In an embodiment, the first storage unit  114  can store the first software of the first storage media  126 . The first storage unit  114  can also store the relevant information, such as data including images, information, sound files, any form of social network day, user profiles, behavior data, cookies, any form of a large collection of user data, or a combination thereof. The first storage unit  114  can be a volatile memory, a nonvolatile memory, an internal memory, an external memory, or a combination thereof. For example, the first storage unit  114  can be a nonvolatile storage such as non-volatile random access memory (NVRAM), non-volatile memory (NVM), non-volatile memory express (NVMe), Flash memory, disk storage, or a volatile storage such as static random access memory (SRAM). 
     In an embodiment, the first storage unit  114  can include a first storage interface  124 . The first storage interface  124  can be used for communication between and other functional units in the first device  102 . The first storage interface  124  can also be used for communication that is external to the first device  102 . The first storage interface  124  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . 
     In an embodiment, the first storage interface  124  can include different implementations depending on which functional units or external units are being interfaced with the first storage unit  114 . The first storage interface  124  can be implemented with technologies and techniques similar to the implementation of the first control interface  122 . 
     In an embodiment, the first communication unit  116  can enable external communication to and from the first device  102 . For example, the first communication unit  116  can permit the first device  102  to communicate with the second device  106  of  FIG. 1 , an attachment, such as a peripheral device or a computer desktop, and the communication path  104 . The first communication unit  116  can also function as a communication hub allowing the first device  102  to function as part of the communication path  104  and not limited to be an end point or terminal unit to the communication path  104 . The first communication unit  116  can include active and passive components, such as microelectronics or an antenna, for interaction with the communication path  104 . 
     In an embodiment, the first communication unit  116  can include a first communication interface  128 . The first communication interface  128  can be used for communication between the first communication unit  116  and other functional units in the first device  102 . The first communication interface  128  can receive information from the other functional units or can transmit information to the other functional units. The first communication interface  128  can include different implementations depending on which functional units are being interfaced with the first communication unit  116 . The first communication interface  128  can be implemented with technologies and techniques similar to the implementation of the first control interface  122 . 
     In an embodiment, the first user interface  118  allows a user (not shown) to interface and interact with the first device  102 . The first user interface  118  can include an input device and an output device. Examples of the input device of the first user interface  118  can include a keypad, a mouse, a touchpad, soft-keys, a keyboard, a microphone, an infrared sensor for receiving remote signals, a virtual display console for remote access, a virtual display terminal for remote access, or any combination thereof to provide data and communication inputs. In an embodiment, the first user interface  118  can include a first display interface  130 . The first display interface  130  can include a display, a projector, a video screen, a speaker, a remote network display, a virtual network display, or any combination thereof. 
     In an embodiment, the first storage interface  124  in a manner similar to the first control interface  122 , can receive, process, transmit, or combination thereof, information from the other functional units, external sources, external destinations, or combination thereof, for a first storage control unit  132 . The first storage control unit  132  can be a processor, an application specific integrated circuit (ASIC), a system on a chip (SOC), an embedded processor, a microprocessor, multi-processors, chip-multiprocessor(s) (CMP), a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), a field programmable gate array (FPGA), or a combination thereof. 
     In an embodiment, the second device  106  can be optimized for implementing an embodiment of the present invention in a multiple device embodiment with the first device  102 . The second device  106  can provide the additional or higher performance processing power compared to the first device  102 . The second device  106  can include a second control unit  134 , a second communication unit  136 , and a second user interface  138 . 
     In an embodiment, the second user interface  138  allows a user (not shown) to interface and interact with the second device  106 . The second user interface  138  can include an input device and an output device. Examples of the input device of the second user interface  138  can include a keypad, a mouse, a touchpad, soft-keys, a keyboard, a microphone, a virtual display console for remote access, a virtual display terminal for remote access, or any combination thereof to provide data and communication inputs. Examples of the output device of the second user interface  138  can include a second display interface  140 . The second display interface  140  can include a display, a projector, a video screen, a speaker, a remote network display, a virtual network display, or any combination thereof. 
     In an embodiment, the second control unit  134  can execute a second software of a second storage media  142  to provide the intelligence of the second device  106  of the electronic system  100 . The second software of the second storage media  142  can operate in conjunction with the first software of the first storage media  126 . The second control unit  134  can provide additional performance compared to the first control unit  112 . The second control unit  134  can operate the second user interface  138  to display information. The second control unit  134  can also execute the second software of the second storage media  142  for the other functions of the electronic system  100 , including operating the second communication unit  136  to communicate with the first device  102  over the communication path  104 . 
     In an embodiment, the second control unit  134  can be implemented in a number of different manners. For example, the second control unit  134  can be a processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a system on a chip (SOC), an embedded processor, a microprocessor, multi-processors, chip-multiprocessor(s) (CMP), a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. The second control unit  134  can include a second controller interface  144 . The second controller interface  144  can be used for communication between the second control unit  134  and other functional units in the second device  106 . The second controller interface  144  can also be used for communication that is external to the second device  106 . 
     In an embodiment, the second controller interface  144  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the second device  106 . The second controller interface  144  can be implemented in different ways and can include different implementations depending on which functional units or external units are being interfaced with the second controller interface  144 . For example, the second controller interface  144  can be implemented with a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), optical circuitry, waveguides, wireless circuitry, wireline circuitry, or a combination thereof. 
     In an embodiment, a second storage unit  146  can store the second software on the second storage media  142 . The second storage unit  146  can also store the relevant information such as data including images, information, sound files, any form of social network day, user profiles, behavior data, cookies, any form of a large collection of user data, or a combination thereof. The second storage unit  146  can be sized to provide the additional storage capacity to supplement the first storage unit  114 . For illustrative purposes, the second storage unit  146  is shown as a single element, although it is understood that the second storage unit  146  can be a distribution of storage elements. Also for illustrative purposes, the electronic system  100  is shown with the second storage unit  146  as a single hierarchy storage system, although it is understood that the electronic system  100  can have the second storage unit  146  in a different configuration. 
     For example, the second storage unit  146  can be formed with different storage technologies forming a memory hierarchal system including different levels of caching, main memory, rotating media, or off-line storage. The second storage unit  146  can be a volatile memory, a nonvolatile memory, an internal memory, an external memory, or a combination thereof. Further for example, the second storage unit  146  can be a nonvolatile storage such as non-volatile random access memory (NVRAM), Flash memory, disk storage, or a volatile storage such as static random access memory (SRAM). 
     In an embodiment, the second storage unit  146  can include a second storage interface  148 . The second storage interface  148  can be used for communication between other functional units in the second device  106 . The second storage interface  148  can also be used for communication that is external to the second device  106 . 
     In an embodiment, the second storage interface  148  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the second device  106 . The second storage interface  148  can include different implementations depending on which functional units or external units are being interfaced with the second storage unit  146 . The second storage interface  148  can be implemented with technologies and techniques similar to the implementation of the second controller interface  144 . The second communication unit  136  can enable external communication to and from the second device  106 . For example, the second communication unit  136  can permit the second device  106  to communicate with the first device  102  over the communication path  104 . 
     In an embodiment, the second communication unit  136  can also function as a communication hub allowing the second device  106  to function as part of the communication path  104  and not limited to be an end point or terminal unit to the communication path  104 . The second communication unit  136  can include active and passive components, such as microelectronics or an antenna, for interaction with the communication path  104 . The second communication unit  136  can include a second communication interface  150 . The second communication interface  150  can be used for communication between the second communication unit  136  and other functional units in the second device  106 . The second communication interface  150  can receive information from the other functional units or can transmit information to the other functional units. 
     In an embodiment, the second communication interface  150  can include different implementations depending on which functional units are being interfaced with the second communication unit  136 . The second communication interface  150  can be implemented with technologies and techniques similar to the implementation of the second controller interface  144 . The first communication unit  116  can couple with the communication path  104  to send information to the second device  106  in the first device transmission  108 . The second device  106  can receive information in the second communication unit  136  from the first device transmission  108  of the communication path  104 . 
     In an embodiment, the second communication unit  136  can couple with the communication path  104  to send information to the first device  102  in the second device transmission  110 . The first device  102  can receive information in the first communication unit  116  from the second device transmission  110  of the communication path  104 . The electronic system  100  can be executed by the first control unit  112 , the second control unit  134 , or a combination thereof. 
     For illustrative purposes, the second device  106  is shown with the partition having the second user interface  138 , the second storage unit  146 , the second control unit  134 , and the second communication unit  136 , although it is understood that the second device  106  can have a different partition. For example, the second software of the second storage media  142  can be partitioned differently such that some or all of its function can be in the second control unit  134  and the second communication unit  136 . Also, the second device  106  can include other functional units not shown in  FIG. 1  for clarity. 
     In an embodiment, the second storage interface  148  in a manner similar to the second control interface  144 , can receive, process, transmit, or combination thereof, information from the other functional units, external sources, external destinations, or combination thereof, for a second storage control unit  152 . The second storage control unit  152  can be a processor, an application specific integrated circuit (ASIC), a system on a chip (SOC), an embedded processor, a microprocessor, multi-processors, chip-multiprocessor(s) (CMP), a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), an field programmable gate array (FPGA), or a combination thereof. 
     In an embodiment, the functional units in the first device  102  can work individually and independently of the other functional units. The first device  102  can work individually and independently from the second device  106  and the communication path  104 . Similarly, the functional units in the second device  106  can work individually and independently of the other functional units. The second device  106  can work individually and independently from the first device  102  and the communication path  104 . For illustrative purposes, the electronic system  100  is described by operation of the first device  102  and the second device  106 . It is understood that the first device  102  and the second device  106  can operate any of the functions, processes, applications, or combination thereof, of the electronic system  100 . 
     In an embodiment, the functions, processes, applications, or combination thereof, described in this application can be at least in part implemented as instructions stored on a non-transitory computer readable medium to be executed by a control unit  112 . The non-transitory computer medium can include the storage unit  114 . The non-transitory computer readable medium can include non-volatile memory, such as a hard disk drive (HDD), non-volatile random access memory (NVRAM), solid-state storage device (SSD), compact disk (CD), digital video disk (DVD), universal serial bus (USB) flash memory devices, Blu-ray Disc™, any other computer readable media, or combination thereof. The non-transitory computer readable medium can be integrated as a part of the electronic system  100  or installed as a removable portion of the electronic system  100 . 
     In an embodiment, the functions, processes, applications, or combination thereof, described in this application can be implemented as instructions stored on a non-transitory computer readable medium to be executed by a first control unit  112 , the second control unit  134 , or a combination thereof. The non-transitory computer medium can include the first storage unit  114 , the second storage unit  146 , or a combination thereof. The non-transitory computer readable medium can include non-volatile memory, such as a hard disk drive (HDD), non-volatile random access memory (NVRAM), solid-state storage device (SSD), compact disk (CD), digital video disk (DVD), universal serial bus (USB) flash memory devices, Blu-ray Disc™, any other computer readable media, or combination thereof. The non-transitory computer readable medium can be integrated as a part of the electronic system  100  or installed as a removable portion of the electronic system  100 . 
     In an embodiment, the functions, processes, applications, or combination thereof, described in this application can be part of the first software of the first storage media  126 , the second software of the second storage media  142 , or a combination thereof. These functions, processes, applications, or combination thereof, can also be stored in the first storage unit  114 , the second storage unit  146 , or a combination thereof. The first control unit  112 , the second control unit  134 , or a combination thereof can execute these functions, processes, applications, or combination thereof, for operating the electronic system  100 . 
     In an embodiment, the electronic system  100  has been described with functions, processes, applications, order, or combination thereof, as an example. The electronic system  100  can partition the functions, processes, applications, or combination thereof, differently or order the functions, processes, applications, or combination thereof, differently. The functions, processes, applications, or combination thereof, described in this application can be hardware implementations, hardware circuitry, or hardware accelerators in the first control unit  112  or in the second control unit  134 . The functions, processes, applications, or combination thereof, can also be hardware implementations, hardware circuitry, or hardware accelerators within the first device  102  or the second device  106  but outside of the first control unit  112  or the second control unit  134 , respectively. 
     Referring now to  FIG. 2 , therein is shown a block diagram of a portion of an electronic learning system  200  of the electronic system  100  in an embodiment of the invention. The electronic learning system  200  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  200  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  200  can provide machine learning including prediction, recommendation, filtering such as spam email filtering, machine learning processes, machine learning functions, or combination thereof. In some embodiments, prediction can be based on clicking or selecting an advertisement or advertiser, can recommend an item or items, can filter spam, can include like processes, or combination thereof, to provide machine learning such as for big data analytics. 
     In an embodiment, the electronic learning system  200  can also include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , or combination thereof. 
     For example, the electronic learning system  200  can include a learning block  210  such as a machine learning block, an initial data block  230  such as a raw data block, a processed data block  250 , a model block  270 , or combination thereof. The processed data block  250  can include a partial data block of the initial data block  230 , an intelligently updated data block of the initial data block  230 , an intelligently selected data block of the initial data block  230 , data with a different format, data with a different data structure, or combination thereof, for further processing or for main machine learning. The learning block  210 , the initial data block  230 , the processed data block  250 , the model block  270 , or combination thereof, can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     For illustrative purposes, the learning block  210 , the initial data block  230 , the processed data block  250 , the model block  270 , or combination thereof, are shown as discrete blocks although it is understood that any of the blocks can share portions of the hardware with any of the other blocks. For example, the initial data block  230 , the processed data block  250 , the model block  270 , or combination thereof, can share portions of hardware memory circuits or components such as the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or combination thereof. 
     In an embodiment, the learning block  210  can provide machine learning, including parallel or distributed processing, cluster computing, or combination thereof. The learning block  210  can be implemented in system processors, storage computing (ISC) processors, the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , the first storage unit  114 , the second storage unit  146 , or combination thereof. 
     In an embodiment, the initial data block  230  can include storage devices with raw, unprocessed, partially processed data, or combination thereof. The initial data block  230  can be implemented in storage devices, memory devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or combination thereof. The processed data block  250  can include storage devices with processed data such as the unprocessed data of the initial data block  230  after processing. The processed data block  250  can be implemented in storage devices, memory devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or combination thereof. 
     In an embodiment, the model block  270  can include storage devices. For example, the storage devices can include data, information, applications, machine learning data, analyzed big data, big data analytics, or combination thereof. The model block  270  can be implemented in storage devices, memory devices, system processors, in storage computing (ISC) processors, the first storage unit  114 , the second storage unit  146 , the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , or combination thereof. 
     It has been discovered that controlling assignments of selected data such as the initial data  230 , the first data  250 , or combination thereof, can provide improved performance. Controlling assignments can include detecting communication resource saturation of input and output (I/O) bandwidth such as the communication path  104  of  FIG. 1 , compute resource saturation of a processor such as a central processing unit (CPU), a control unit, the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , or combination thereof. For example, computation of the initial data  230  to the first data  250  can cause bandwidth saturation, such as limitations of capacity, with the first control unit  112 , the second control unit  134 , the communication path  104 , interfaces, or combination thereof, which can be addressed by a preprocessing block, further described below, for assigning or allocating processing between compute devices and storage devices based on system information. 
     For illustrative purposes, the learning block  210 , the initial data block  230 , the processed data block  250 , the model block  270 , or combination thereof, are shown as discrete blocks although it is understood that any number, combination, distribution, division, partitioning, or combination thereof, of the blocks may be included. For example, the learning block  210  can include multiple blocks distributed across multiple devices. Additional details are provided in subsequent figure descriptions for embodiments of the learning block  210 , the initial data block  230 , the processed data block  250 , the model block  270 , or combination thereof. 
     Referring now to  FIG. 3 , therein is shown a block diagram of a portion of an electronic learning system  300  of the electronic system  100  in an embodiment of the invention. In a manner similar to the electronic system  200  of  FIG. 2 , the electronic learning system  300  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  300  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  300  can provide machine learning including prediction, recommendation, filtering such as spam email filtering, machine learning processes, machine learning functions, or combination thereof. In some embodiments, prediction, for example, can be based on clicking or selecting an advertisement or advertiser, can recommend an item or items, can filtering spam, can include like processes, or combination thereof, to provide machine learning such as for big data analytics. 
     In an embodiment, the electronic learning system  300  can also include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  300  can include learning devices  310 , such as machine learning devices, including a first learning device  312 , a second learning device  314 , a third learning device  316 , or combination thereof. The electronic learning system  300  can also include data blocks  350 , such as training data block, including a first data block  352 , a second data block  354 , a third data block  356 , or combination thereof. The electronic learning system  300  can further include model devices  370  including a first model device  372 , a second model device  374 , or combination thereof. The devices  310 , the data blocks  350 , the model devices  370 , or combination thereof, are implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     For example, the devices  310  can be implemented with the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , or combination thereof. The data blocks  350  can be implemented with the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or combination thereof. The model devices  370  can be implemented with the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , the first storage unit  114 , the second storage unit  146 , or combination thereof. For illustrative purposes, the machine learning devices  310 , the data blocks  350 , the model devices  370 , or combination thereof, are shown as discrete blocks although it is understood that any of the blocks can share portions of the hardware with any of the other blocks in a manner similar to the electronic learning system  200 . 
     In an embodiment, the electronic learning system  300  can include a put model process  390  for updating, amending, revising, replacing, writing, recording, entering, or combination thereof, a model or model parameter  376  such as the first model device  372 , the second model device  374 , or combination thereof. The model can be updated, amended, revised, replaced, written, recorded, entered, or combination thereof, with the machine learning devices  310  on the model devices  370 . 
     In an embodiment, the put model process  390 , machine learning processes, big data process, any other process, or combination thereof, can include a network  394 . For example, the network  394  can include any of a variety of networks in a manner similar to the communication path  104  of  FIG. 1  and can include network transmissions  396 , network capacity  398 , or combination thereof. The network transmissions  396  can transmit the model or the model parameter  376 . The network capacity  398  can include a quantity of the network transmissions  396  supported by the network  394 . 
     In an embodiment, the learning system  300  can provide detecting, identifying, monitoring, measuring, checking, or combination thereof, of the network  394 , the network transmissions  396 , network capacity  398 , or combination thereof. The learning system  300  including the learning devices  310 , the data blocks  350 , the model devices  370 , or combination thereof can detect or identify issues or bottlenecks with the network capacity  398 . 
     It has been discovered that the learning system  300  can detect bottlenecks in parallel or distributed machine learning systems. The learning system  300  can at least detect or identify issues with the network capacity  398 . 
     Referring now to  FIG. 4 , therein is shown block diagram of a portion of an electronic learning system  400  of the electronic system  100  in an embodiment of the invention. In a manner similar to the electronic system  200  of  FIG. 2 , the electronic learning system  400  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  400  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  400  can provide machine learning including prediction, recommendation, filtering such as spam email filtering, machine learning processes, machine learning functions, or combination thereof. In some embodiments, prediction can be based on clicking or selecting an advertisement or advertiser, can recommend an item or items, can filter spam, can include like processes, or combination thereof, to provide machine learning such as for big data analytics. 
     In an embodiment, the electronic learning system  400  can also include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  400  can include learning devices  410 , such as machine learning devices, in a manner similar to the machine learning devices  310  of  FIG. 3 , including a first learning device  412 , a second learning device  414 , a third machine device  416 , or combination thereof. The electronic learning system  400  can also include data blocks  450 , such as training data blocks, in a manner similar to the data blocks  350  of  FIG. 3 , including a first data block  452 , a second data block  454 , a third data block  456 , or combination thereof. 
     In an embodiment, the electronic learning system  400  can include model devices  470 , such as model servers, in a manner similar to the model devices  370  of  FIG. 3 , including a first model device  472 , a second model device  474 , or combination thereof. The machine learning devices  410 , the training data blocks  450 , the model devices  470 , or combination thereof, can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     For example, the devices  410  can be implemented with the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , or combination thereof. The data blocks  450  can be implemented with the first storage unit  114 , the second storage unit  146 , or combination thereof. The model devices  470  can be implemented with the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , the first storage unit  114 , the second storage unit  146 , or combination thereof. For illustrative purposes, the machine learning devices  410 , the training data blocks  450 , the model devices  470 , or combination thereof, are shown as discrete blocks although it is understood that any of the blocks can share portions of the hardware with any of the other blocks in a manner similar to the electronic learning system  200 . 
     In an embodiment, the electronic learning system  400  can include a get model process  490  for extracting, acquiring, attaining, accessing, requesting, receiving, or combination thereof, a model or model parameter  476  such as the first model device  472 , the second model device  474 , the first model device  372 , the second model device  374 , or combination thereof. The model can be extracted, acquired, attained, accessed, requested, received, or combination thereof, with the learning devices  410 , the learning devices  310 , from the model devices  470  or the model devices  370 . 
     In an embodiment, the get model process  490 , machine learning processes, big data process, any other process, or combination thereof, can include a network  494 . The network  494  can include a variety of networks in a manner similar to the communication path  104  of  FIG. 1  and can include network transmissions  496 , network capacity  498 , or combination thereof. The network transmissions  496  can transmit the model or the model parameter  476 . The network capacity  498  can include a quantity of the network transmissions  496  supported by the network  494 . 
     In an embodiment, the learning system  400  can provide detecting, identifying, monitoring, measuring, checking, or combination thereof, of the network  494 , the network transmissions  496 , network capacity  498 , or combination thereof. The learning system  400  including the learning devices  410 , the data blocks  450 , the model devices  470 , or combination thereof can detect or identify issues or bottlenecks with the network capacity  498 . 
     In an embodiment, the electronic learning system  400  can include the get model process  490 , a put model process (not shown) in a manner similar to the put model process  390  of  FIG. 3 , machine learning processes, big data process, any other process, or combination thereof. These processes can provide combined issues, bottlenecks, or combination thereof, for the network  494 . Similarly, in an embodiment, the electronic learning system  300  of  FIG. 3  can include the get model process  490 , the put model process  390 , machine learning processes, big data process, any other process, or combination thereof, can provide combined issues, bottlenecks, or combination thereof, for the network  394  of  FIG. 3 . 
     For illustrative purposes the electronic learning system  400  and the electronic learning system  300  are shown as discrete systems although it is understood that the electronic learning system  400 , the electronic learning system  300 , other system, or combination thereof, can be combined in part or as a whole. For example, the electronic learning system  400  and the electronic learning system  300  can represent one system with different modes. 
     It has been discovered that the learning system  400  can detect bottlenecks in parallel or distributed machine learning systems. The learning system  400  can at least detect or identify issues with the network capacity  498 . 
     Referring now to  FIG. 5 , therein is shown a block diagram of a portion of an electronic learning system  500  of the electronic system  100  in an embodiment of the invention. In a manner similar to the electronic system  200  of  FIG. 2 , the electronic learning system  500  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  500  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  500  can provide machine learning including scanning, filtering, predicting, recommending, machine learning processes, machine learning functions, big data analytics, or combination thereof. The electronic learning system  500  can also include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  500  can include a system device  512 , a storage device  514 , first system data  552 , such as training data, first storage data  554 , such as training data, or combination thereof. The electronic learning system  500  can also include second system data  562 , such as training data, updated system data of the first system data  552 , partial data of the first system data  552 , intelligently selected data of the first system data  552 , intelligently updated data of the first system data  552 , or combination thereof, second storage data  564 , such as training data, intelligently updated storage data of the first storage data  554 , partial data of the first storage data  554 , intelligently selected data of the first storage data  554 , or combination thereof. The electronic learning system  500  can provide a model device  370 . 
     In an embodiment, the electronic learning system  500  can include network transmissions  596 , network capacity  598 , or combination thereof. The learning system  500  can provide detecting, identifying, monitoring, measuring, checking, or combination thereof, of the network transmissions  596 , network capacity  598 , or combination thereof. The learning system  500  including the system device  512 , the storage device  514 , the first system data  552 , the first storage data  554 , the second system data  562 , the second storage data  564 , the model device  370 , or combination thereof can detect or identify issues or bottlenecks with the network capacity  398 . 
     It has been discovered that the learning system  500  can detect bottlenecks in parallel or distributed machine learning systems. The learning system  500  can at least detect or identify issues with the network capacity  598 . 
     Referring now to  FIG. 6 , therein is shown a block diagram of a portion of an electronic learning system  600  of the electronic system  100  in an embodiment of the invention. The electronic learning system  600  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  600  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  600  can provide machine learning including statistical machine learning such as a gradient descent optimization method including a Stochastic Gradient Descent (SGD). The Stochastic Gradient methods can provide faster convergence rates, improved step sizes, running multiple processors independently in parallel, or combination thereof. The Stochastic Gradient methods can also include a Stochastic Averaged Gradient algorithm providing fast initial convergence for stochastic methods, fast late-stage convergence of full-gradient methods keeping cheap iteration cost of stochastic gradients, or combination thereof. 
     In an embodiment, the electronic learning system  600  can include a select block  640  that can provide machine learning processes such as data selection, batch selection, random batch selection, or combination thereof. The select block  640  can receive, process, select, partition, transmit, or combination thereof. For example, the select block  640  can process first data  652  such as training data, second data  656  such as partial data of the first data  652 , intelligently updated data of the first data  652 , intelligently selected data of the first data  652 , or combination thereof Also for example, the select block  640  can select or partition the second data  656  from the first data  652 . 
     In an embodiment, the electronic learning system  600  can also include a compute block  660  for computing a gradient  664 , an update block  670  for updating at least a vector  674  of a model, or combination thereof. The update block  670  can also update an entirety of the model including vectors  674 . The select block  640 , the compute block  660 , and the update block  670  can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  600  with the select block  640 , the compute block, the update block  670 , or combination thereof, can provide machine learning including big data machine learning, big data analytics, or combination thereof. The select block  640 , the compute block, the update block  670 , or combination thereof, can select, partition, or combination thereof, data, processes, functions, or combination thereof. The selection, partition, or combination thereof, can provide offloading, such as offload of machine learning processes or machine learning functions to in storage computing (ISC). The offloading can provide distributing or partitioning of functions with high computation complexity, high input and output (I/O) overhead, or combination thereof. 
     For illustrative purposes, the select block  640 , the compute block  660 , the update block  670 , or combination thereof, are shown as discrete blocks although it is understood that any of the blocks can share portions of the hardware with any of the other blocks. For example, the select block  640 , the compute block  660 , the update block  670 , or combination thereof, can share portions of hardware memory circuits or components such as the first control unit  112 , the second control unit  134 , the first storage unit  114 , the second storage unit  146 , or combination thereof. 
     Further for illustrative purposes, the select block  640 , the compute block  660 , the update block  670 , or combination thereof, are shown as discrete blocks, although it is understood that any number, combination, distribution, division, partitioning, or combination thereof, of the blocks may be included. For example, the select block  640 , the compute block  660 , the update block  670 , or combination thereof, can each include multiple blocks distributed across multiple devices. 
     It has been discovered that the electronic learning system  600  of the electronic system  100  with the select block  640 , the compute block  660 , and the update block  670 , or combination thereof, can provide offload of machine learning processes or machine learning functions. Functions with high computation complexity, high input and output (I/O) overhead, or combination thereof, can be at least partially distributed or partitioned to in storage computing (ISC). 
     Referring now to  FIG. 7  therein is shown a block diagram of a portion of an electronic learning system  700  of the electronic system  100  in an embodiment of the invention. The electronic learning system  700  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  700  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  700  can provide machine learning including intelligently selecting or partitioning data, such as training data, for providing significantly improved partial data for machine learning including parallel or distributed machine learning. The significantly improved partial data can improve updating a model, vector, or combination thereof. The intelligent selection or partitioning can provide faster convergence with increased input and output (I/O). 
     For example, in a manner similar to the electronic learning system  600 , the electronic learning system  700  can provide statistical machine learning such as statistical machine learning such as gradient descent optimization method including a Stochastic Gradient Descent (SGD). The Stochastic Gradient methods, including a Stochastic Averaged Gradient algorithm, can provide a faster convergence rate, improved step sizes, multiple processors independently run in parallel, fast initial convergence, fast late-stage convergence of full-gradient methods keeping cheap iteration cost of stochastic gradients, or combination thereof. 
     In an embodiment, the electronic learning system  700  can include a scan and select block  730  that can provide machine learning processes such as scanning, selecting, or combination thereof. The scan and select block  730  can receive, scan, process, select, partition, transmit, or combination thereof. For example, the scan and select block  730  can intelligently process, including scan, select, partition, data selection, batch selection, random batch selection, or combination thereof, first data  752  such as raw data, input data for preprocessing training data, second data  754  such as preprocessed data, intelligently selected training data of the first data  752 , partial data of the first data  752 , intelligently updated data of the first data  752 , intelligently selected data of the first data  752 , or combination thereof. Also for example, the scan block  730  can intelligently select or partition the second data  754  from the first data  752 . 
     In an embodiment, the electronic learning system  700  can also include a compute block  760  for computing a gradient  764 , an update block  770  for updating at least a vector  774  of a model including vectors  774 . The update block  770  can also update an entirety of the model. The scan and select block  730 , the compute block  760 , and the update block  770  can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     For example, the electronic learning system  700  with the scan and select block  730 , the compute block, the update block  770 , or combination thereof, can provide machine learning including big data machine learning, big data analytics, or combination thereof. The scan and select block  730 , the compute block, the update block  770 , or combination thereof, can select, partition, or combination thereof, data, processes, functions, or combination thereof. The selection, partition, or combination thereof, can provide offloading, such as offload of machine learning processes or machine learning functions to in storage computing (ISC). The offloading can provide distributing or partitioning of functions with high computation complexity, high input and output (I/O) overhead, or combination thereof. 
     For illustrative purposes, the scan and select block  730 , the compute block  760 , the update block  770 , or combination thereof, are shown as discrete blocks although it is understood that any of the blocks can share portions of the hardware with any of the other blocks. For example, the scan and select block  730 , the compute block  760 , the update block  770 , or combination thereof, can share portions of hardware memory circuits or components such as the first control unit  112 , the second control unit  134 , the first storage unit  114 , the second storage unit  146 , or combination thereof. 
     Further for illustrative purposes, the scan and select block  730 , the compute block  760 , the update block  770 , or combination thereof, are shown as are shown as discrete blocks although it is understood that any number, combination, distribution, division, partitioning, or combination thereof, of the blocks may be included. For example, the scan and select block  730 , the compute block  760 , the update block  770 , or combination thereof, can each include multiple blocks distributed across multiple devices. 
     It has been discovered that the electronic learning system  700  of the electronic system  100  with the scan and select block  730 , the compute block  760 , and the update block  770 , or combination thereof, can intelligently provide offload of machine learning processes or machine learning functions, particularly functions with increased input and output (I/O). Functions with high computation complexity, high input and output (I/O) overhead, or combination thereof, can be at least partially distributed or partitioned to in storage computing (ISC). 
     Referring now to  FIG. 8  therein is shown a block diagram of a portion of an electronic learning system  800  of the electronic system  100  in an embodiment of the invention. The electronic learning system  800  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  800  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  800  can provide machine learning including prediction, recommendation, filtering such as spam email filtering, machine learning processes, machine learning functions, or combination thereof. In some embodiments, prediction can include clicking or selecting an advertisement or advertiser, can recommend an item or items, can filter spam, can include like processes, or combination thereof, to provide machine learning such as for big data analytics. 
     For example, the electronic learning system  800  can include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  800  can include a learning block  810  such as a machine learning block for implementing or executing machine learning algorithms, big data machine learning, big data analytics, or combination thereof. The learning block  810  can implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     For example, the learning block  810  can provide machine learning, including parallel or distributed processing, cluster computing, or combination thereof. The learning block  810  can be implemented in system processors, in storage computing (ISC) processors, the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , the first storage unit  114 , the second storage unit  146 , or combination thereof. 
     In an embodiment, the electronic learning system  800  can include a preprocessing block  830  such as scan and filter block for machine learning processes including scanning, filtering, selecting, partitioning, processing, receiving, transmitting, or combination thereof, in a manner similar to the select block  640  of  FIG. 6 , the scan and select block  730  of  FIG. 7 , or combination thereof. The preprocessing block  830  can intelligently select or partition data, such as training data, for providing significantly improved partial data. The significantly improved partial data can improve updating a model, vector, or combination thereof. The intelligent selection or partitioning can provide faster convergence with increased input and output (I/O). The preprocessing block  830  can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the preprocessing block  830  can process, scan, filter, select, partition, receive, transmit, or combination thereof, data including a first data block  854  such as training data, a second data block  856 , or combination thereof. The second data block  856  can include intelligently selected data of the first data block  854 , intelligently updated data of the first data block  854 , partial data of the first data block  854 , or combination thereof. The first data block  854 , the second data block  856 , or combination thereof, are implemented at least in part in hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or a combination thereof. 
     For example, the preprocessing block  830  can process, scan, filter, select, partition, receive, transmit, or combination thereof, a portion of or the entirety of the first data block  854  for providing the second data block  856 . The first data block  854  can include raw data, training data, initial data, or combination thereof. The preprocessing block  830  can scan and filter a portion of or the entirety of the first data block  854  for providing a portion of or the entirety of the second data block  856 , which can include filtered data, selected data, partitioned data, training data, partial data, partial training data, or combination thereof. 
     In an embodiment, the learning block  810  can process, scan, filter, select, partition, receive, transmit, or combination thereof, a portion of or the entirety of the second data block  856 , for providing at least a portion of a model block  870 , which can include a model parameter, vectors, an entire model, or combination thereof. The model  870  can be implemented at least in part in hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or a combination thereof. 
     In an embodiment, the model block  870  can include storage devices with machine learning data, analyzed big data, big data analytics, or combination thereof. The model block  870  can be implemented in storage devices, memory devices, system processors, storage computing (ISC) processors, the first storage unit  114 , the second storage unit  146 , the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , or combination thereof. 
     For example, the first data block  854  can include storage devices with unprocessed or partially processed data. The first data block  854  can be implemented in storage devices, memory devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or combination thereof. The second data block  856  can include storage devices with processed data such as the unprocessed data of the first data block  854  after processing. The second data block  856  can be implemented in storage devices, memory devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or combination thereof. 
     For illustrative purposes, the learning block  810 , the preprocessing block  830 , the first data block  854 , the second data block  856 , the model block  870 , or combination thereof, are shown as discrete blocks although it is understood that any of the blocks can share portions of the hardware with any of the other blocks. For example, the first data block  854 , the second data block  856 , the model block  870 , or combination thereof, can share portions of hardware memory circuits or components such as the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or combination thereof. 
     Further for illustrative purposes, the learning block  810 , the preprocessing block  830 , the model block  870 , or combination thereof, are shown as discrete blocks although it is understood that any number, combination, distribution, division, partitioning, or combination thereof, of the blocks may be included. For example, the learning block  810 , the preprocessing block  830 , the model block  870 , or combination thereof, can each include multiple blocks distributed across multiple devices. 
     It has been discovered that the electronic learning system  800  with the learning block  810 , the preprocessing block  830 , the first data block  854 , the second data block  856 , the model block  870 , or combination thereof, can provide machine learning, including parallel or distributed processing, cluster computing, or combination thereof. The electronic learning system  800  can implement the preprocessing block  830 , the model block  870 , or combination thereof, in a system processor, in storage computing (ISC), or combination thereof. 
     Referring now to  FIG. 9  therein is shown a block diagram of a portion of an electronic learning system  900  of the electronic system  100  in an embodiment of the invention. The electronic learning system  900  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  900  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  900  can include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     For illustrative purposes, the electronic learning system  900  is shown with a learning block  910  as part of a system device  912  although it is understood that a storage device  914  may also include other blocks including a learning block such as the learning block  910 . The system device  912  can also include other blocks. 
     In an embodiment, the electronic learning system  900  can include a preprocessing block  930  such as a scan and filter block for scanning, filtering, selecting, partitioning, processing, receiving, transmitting, or combination thereof. The preprocessing block  930  can intelligently select or partition data, such as training data, for providing significantly improved partial data. The significantly improved partial data can improve updating a model, vector, or combination thereof. The intelligent selection or partitioning can provide faster convergence with increased input and output (I/O). The preprocessing block  930  can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     For example, the preprocessing block  930  can process data including a first data block  954  such as training data, a second data block  956 , or combination thereof. The second data block  956  can include intelligently selected data of the first data block  954 , intelligently updated data of the first data block  954 , partial data of the first data block  954 , or combination thereof. The preprocessing block  930  can process data received from the first data block  954  and provide data for the second data block  956 . The first data block  954 , the second data block  956 , or combination thereof, are implemented at least in part in hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or a combination thereof. 
     In an embodiment, the learning block  910  can process, analyze, predict, recommend, filter, learn, receive, transmit, or combination thereof, data of the second data block  956 . For example, the electronic learning system  900  can provide prediction, recommendation, filtering, machine learning processes, machine learning functions, or combination thereof. In some embodiments, prediction for example, can be based on clicking or selecting an advertisement or advertiser, can recommend an item or items, can filter spam, can include like processes, or combination thereof, to provide parallel or distributed machine learning such as for big data analytics. 
     It has been discovered that the electronic learning system  900  with the learning block  910 , the preprocessing block  930 , the first data block  954 , the second data block  956 , the system device  912 , the storage device  914 , or combination thereof, can provide machine learning, including parallel or distributed processing, cluster computing, or combination thereof. The preprocessing block  930 , the model block  970 , or combination thereof, can be implemented in the system device  912 , the storage device  914 , or combination thereof. 
     Referring now to  FIG. 10 , therein is shown a block diagram of a portion of an electronic learning system  1000  of the electronic system  100  in an embodiment of the invention. The electronic learning system  1000  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. For example, preprocessing can be executed by storage devices or system devices such as a system control unit or central processing unit (CPU) based on the electronic system  100  analyzing circumstances or conditions to determine allocation or assignment as further described below. 
     In an embodiment, the electronic learning system  1000  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  1000  can provide machine learning including prediction, recommendation, filtering, machine learning processes, machine learning functions, or combination thereof. In some embodiments, prediction, for example, can be based on clicking or selecting an advertisement or advertiser, can recommend an item or items, can filter spam, can include like processes, or combination thereof, to provide parallel or distributed machine learning such as for big data analytics. 
     For example, the electronic learning system  1000  can include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  1000  can include a system device  1012 , a storage device  1014 , an interface  1016 , or combination thereof. The system device  1012  and the storage device  1014  can communicate with each other or within each device through the interface  1016 . The system device  1012 , the storage device  1014 , and the interface  1016  can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), wiring, traces, passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  1000  can include learning blocks  1020  such as machine learning blocks for implementing or executing machine learning algorithms, big data machine learning, big data analytics, or combination thereof. The electronic learning system  1000  can include a storage learning block  1024 , a system learning block  1028 , or combination thereof. The storage learning block  1024 , the system learning block  1028 , or combination thereof, can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     For example, the storage learning block  1024 , the system learning block  1028 , or combination thereof, can provide machine learning, including parallel or distributed processing, cluster computing, or combination thereof. The storage learning block  1024 , the system learning block  1028 , or combination thereof, can be implemented in system processors, storage computing (ISC) processors, the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , the first storage unit  114 , the second storage unit  146 , or combination thereof. 
     In an embodiment, the electronic learning system  1000  can include a preprocessing block  1030  such as scan and filter block for scanning, filtering, selecting, partitioning, processing, receiving, transmitting, or combination thereof. The preprocessing block  1030  can intelligently select or partition data, such as training data, for providing significantly improved partial data. The significantly improved partial data can improve updating a model, vector, or combination thereof. The intelligent selection or partitioning can provide faster convergence with increased input and output (I/O). The preprocessing block  1030  can implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the preprocessing block  1030  can also be implemented with a storage control unit  1032 . The storage control unit  1032 , in a manner similar to the first storage control unit  132  or the second storage control unit  152 , can be a processor, an application specific integrated circuit (ASIC) an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. Similarly, a system control unit  1034 , in a manner similar to the second control unit  134  or the first control unit  112 , can also be a processor, an application specific integrated circuit (ASIC) an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), a field programmable gate array (FPGA), or a combination thereof. 
     In an embodiment, the preprocessing block  1030  of the storage device  1014  can offload a function  1040  such as scan, filter, learn, or combination thereof, from the system control unit  1034  to the storage control unit  1032 . Similarly, a host can offload the function  1040  to the storage control unit  1032 . The functions  1040  can include machine learning, big data processing, analyzing big data, big data analytics, or combination thereof. The function  1040 , system device  1012 , the storage device  1014 , or combination thereof, can provide a scheduling algorithm for distributed machine learning with the storage device  1014  such as a smart solid-state storage device (SSD). 
     In an embodiment, the electronic learning system  1000  can include or define functions  1040  including offloadable machine learning components or functions. The offloadable machine learning components or functions can include main machine learning algorithms, filter, scan, any machine learning, or combination thereof. The electronic learning system  1000  can also divide, partition, select, or combination thereof, the offloadable machine learning components or functions. The offloadable machine learning components or functions can be divided, partitioned, selected, or combination thereof, for one or more iterations of machine learning. The electronic learning system  1000  can provide monitoring for dynamic scheduling. 
     For illustrative purposes, the electronic learning system  1000  is shown with the preprocessing block  1030  within the storage device  1014  although it is understood that the system device  1012  may also include functions or blocks for scan, filter, the preprocessing block  1030 , or combination thereof. The storage device  1014 , the system device  1012 , or combination thereof may also include any number or type of blocks. 
     For example, the preprocessing block  1030  can process data, such as training data, selected data, or combination thereof, including a first data block  1052 , a second data block  1054 , a third data block  1056 , or combination thereof. The preprocessing block  1030  can process, select, partition, or combination thereof, data received from the first data block  1052 , such as a training data block, and provide data for the second data block  1054 , the third data block  1056 , or combination thereof. The second data block  1054 , the third data block  1056 , or combination thereof, can include an intelligently selected data block of the first data block  1052 , an intelligently updated data block of the first data block  1052 , a partial data block of the first data block  1052 , or combination thereof. 
     In an embodiment, the first data block  1052 , the second data block  1054 , the third data block  1056 , or combination thereof, can provide selected data for machine learning. The first data block  1052 , the second data block  1054 , the third data block  1056 , or combination thereof, can be implemented at least in part in hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or a combination thereof. 
     In an embodiment, the storage learning block  1024  can process, analyze, predict, recommend, filter, learn, receive, transmit, or combination thereof, data of the third data block  1056 . For example, the electronic learning system  1000  can provide prediction, recommendation, filtering, machine learning processes, machine learning functions, or combination thereof. In some embodiments, prediction, for example, can be based on clicking or selecting an advertisement or advertiser, can recommend an item or items, can filter spam, can include like processes, or combination thereof, to provide parallel or distributed machine learning such as for big data analytics. 
     In an embodiment, the system device  1012  of the electronic learning system  1000  can include a programming interface  1082 , which can include an application programming interface (API), for machine learning including machine learning processes outside the system device  1012 . For example, the programming interface  1082  can include programming languages, C, C++, scripting languages, Perl, Python, or combination thereof. The programming interface  1082  can apply to data, including big data, for all machine learning processes or algorithms including processing, scanning, filtering, analyzing, compressing, Karush-Kuhn-Tucker (KKT) filtering, Karush-Kuhn-Tucker (KKT) vector error filtering, random filtering, predicting, recommending, learning, or combination thereof. 
     In an embodiment, the system device  1012 , the storage device  1014 , or combination thereof, can identify issues including a bottleneck, resource saturation, pressure, limiting resource, or combination thereof. For example, the interface  1016  can include saturated input and output (I/O), which can provide reduced I/O for the electronic learning system  1000 . The system device  1012 , the storage device  1014 , or combination thereof, can identify the saturated input and output (I/O) of the interface  1016  and intelligently offload, throttle, parallelize, distribute, or combination thereof, processing, including the functions  1040  to the storage control unit  1032 , the system control unit  1034 , or combination thereof. 
     In an embodiment, the system device  1012 , the storage device  1014 , or combination thereof, with the intelligent offloading, throttling, paralleling, distributing, or combination thereof, can alleviate issues including the bottleneck, resource saturation, pressure, limiting resource, or combination thereof. Alleviating issues can provide significantly higher throughput for the electronic learning system  1000 , the electronic system  100 , or combination thereof. 
     For example, the programming interface  1082  can include a “LEARN” command. The “LEARN” command can include information including metadata for executing the programming interface  1082  with the functions  1040  for parallel or distributed processing. The system device  1012 , the storage device  1014 , or combination thereof, can include a scheduler block  1084 , which can include a high level programming language, for implementing the functions  1040 , scheduling algorithms, assigning functions, assigning data, or combination thereof. 
     In an embodiment, the scheduler block  1084  can include an ad-hoc style per-node scheduler and access a control unit of the storage device  1014 , such as the first storage control unit  132 , the second storage control unit  152 , or combination thereof, for selecting, partitioning, dividing, or combination thereof, machine learning with a control unit of the system device  1012 , such as the first control unit  112 , the second control unit  134 , or combination thereof. The electronic learning system  1000  can provide SSD-runnable binaries for the preprocessing block  1030  for offloading scan and filter, a main iterative algorithm, or combination thereof, based on an I/O bottleneck, such as saturated I/O, to alleviate pressure. 
     For example, the preprocessing block  1030  can filter additional data including the first data  1052  based on determining a bottleneck such as the I/O bottleneck, the saturated I/O, or combination thereof. The preprocessing block  1030  can filter less data including the first data  1052  based on determining there is additional bandwidth including bandwidth in the interface  1016 . The electronic learning system  1000  with the scheduler block  1084  can determine offloading at least a portion of the main iterative algorithm if a further I/O bottleneck is detected after offloading scan and filter. Data can be retrieved by the system device  1012 , the storage device  1014 , or combination thereof, randomly, regularly, or combination thereof, for determining I/O bottlenecks. 
     In an embodiment, the programming interface  1082 , the scheduler block  1084 , or combination thereof, can also include system calls such as new system calls, extended system calls, or combination thereof, for implementing the programming interface  1082 , the functions  1040 , or combination thereof. The programming interface  1082 , the functions  1040 , or combination thereof, can be implemented based on system information  1086 . Further, the preprocessing block  1030  can intelligently select, partition, filter, or combination thereof, the first data  1052  based on the system information  1086 . 
     In an embodiment, the system information  1086  can include system utilization, system bandwidth, system parameters, input and output (I/O) utilization, memory utilization, memory access, storage utilization, storage access, control unit utilization, control unit access, central processing unit (CPU) utilization, CPU access, memory processor utilization, memory processor access, or combination thereof. The system information  1086  can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, the first storage media  126 , the second storage media  142 , or a combination thereof. 
     In an embodiment, the programming interface  1082 , the functions  1040 , the scheduler block  1084 , or combination thereof, can also communicate with a service interface  1088 , which can include a web service interface. For example, the service interface  1088  can provide communication with services external to the electronic learning system  1000 , the electronic system  100 , or combination thereof. The service interface  1088  can communicate with architectures for building client-server applications such as representational state transfer (REST), protocol specifications for exchanging data such as simple object access protocol (SOAP), services external to the electronic system  100 , protocols external to the electronic system  100 , architectures external to the electronic system  100 , or combination thereof. 
     In an embodiment, the storage device  1014  can include Flash, solid-state drives (SSD), phase-change memory (PCM), spin-transfer torque random access memory (STT-RAM), resistive random access memory (ReRAM), magentoresistive random access memory (MRAM), any storage device, any memory device, or combination thereof. Storage devices  1014  and system devices  1012  can connect with the interface  1016  including memory bus, serial attached small computer system interface (SAS), serial attached advanced technology attachment (SATA), non-volatile memory express (NVMe), Fiber channel, Ethernet, remote direct memory access (RDMA), any interface, or combination thereof. 
     For illustrative purposes, the electronic learning system  1000  is shown with one of each of the components although it is understood that any number of the components may be included. For example, the electronic learning system  1000  can include more than one of the storage device  1014 , the first data block  1052 , the second data block  1054 , the third data block  1056 , any component, or combination thereof. Prediction, recommendation, filtering, machine learning processes, machine learning functions, or combination thereof, can provide machine learning such as for big data analytics. Similarly, the system learning block  1028 , the storage learning block  1024 , or combination thereof, can process, analyze, predict, recommend, filter, learn, receive, transmit, or combination thereof, data of the second data block  1054  for prediction, recommendation, filtering, machine learning processes, machine learning functions, or combination thereof. 
     It has been discovered that the electronic learning system  1000  of the electronic system  100  with system device  1012 , the storage device  1014 , can identify and alleviate issues with intelligent processing offload, throttling, paralleling, distribution, or combination thereof. The intelligent process offloading, throttling, paralleling, distributing, or combination thereof, can significantly improve performance by at least avoiding interface  1016  bottlenecks, resource saturation, pressure, or combination thereof, utilizing higher bandwidth in the storage device  1014  for the functions  1040 . 
     Referring now to  FIG. 11  therein is shown a block diagram of a portion of an electronic learning system  1100  of the electronic system  100  in an embodiment of the invention. The electronic learning system  1100  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  1100  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  1100  provide machine learning including prediction, recommendation, filtering, machine learning processes, machine learning functions, or combination thereof. In some embodiments, prediction, for example, can be based on clicking or selecting an advertisement or advertiser, can recommend an item or items, can filter spam, can include like processes, or combination thereof, to provide parallel or distributed machine learning such as for big data analytics. 
     For example, the electronic learning system  1100  can include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  1100  can include a system device  1112 , a storage device  1114 , an interface  1116 , or combination thereof. The system device  1112  and the storage device  1114  can communicate with each other or within each device through the interface  1116 . The system device  1112 , the storage device  1114 , and the interface  1116  can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), wiring, traces, passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  1100  can include learning blocks  1120  such as a machine learning block for implementing or executing machine learning algorithms, big data machine learning, big data analytics, or combination thereof. The electronic learning system  1100  can include a storage learning block  1124 , a system learning block  1128 , or combination thereof. The storage learning block  1124 , the system learning block  1128 , or combination thereof, can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     For example, the storage learning block  1124 , the system learning block  1128 , or combination thereof, can provide machine learning, including parallel or distributed processing, cluster computing, or combination thereof. The storage learning block  1124 , the system learning block  1128 , or combination thereof, can be implemented in system processors, in storage computing (ISC) processors, the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , the first storage unit  114 , the second storage unit  146 , or combination thereof. 
     In an embodiment, the electronic learning system  1100  can include an adaptive block  1130  such as an adaptive preprocessing block, which can include a block for scanning, filtering, selecting, partitioning, processing, receiving, transmitting, or combination thereof. The adaptive block  1130 , such as a scan and filter block, can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. For example, the adaptive block  1130  as an adaptive preprocessing block can intelligently select or partition data, such as training data, based on dynamic system behavior monitoring for providing significantly improved partial data, which can improve updating a model, vector, or combination thereof, and can provide faster convergence with increased input and output (I/O). 
     In an embodiment, the adaptive block  1130  can also be implemented with a storage control unit  1132 . The storage control unit  1132 , in a manner similar to the first storage control unit  132  or the second storage control unit  152 , can be a processor, an application specific integrated circuit (ASIC) an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), a field programmable gate array (FPGA), or a combination thereof. Similarly, a system control unit  1134 , in a manner similar to the second control unit  134  or the first control unit  112 , can also be a processor, an application specific integrated circuit (ASIC) an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), a field programmable gate array (FPGA), or a combination thereof. 
     In an embodiment, the adaptive block  1130  can include a selection block  1140 , which can calculate an adaptive selection ratio, an adaptive learning ratio, the gradient  764  of  FIG. 7 , or combination thereof. The adaptive block  1130  of the storage device  1114  can offload functions including the functions  1040  of  FIG. 10 , such as scan, filter, select, partition, process, receive, transmit, learn, or combination thereof, from the system control unit  1134  to the storage control unit  1132 , from the storage control unit  1132  to the system control unit  1134 , or combination thereof. The functions can include machine learning, big data processing, analyzing big data, big data analytics, or combination thereof. For example, the adaptive block  1130  can determine system resource utilization and storage bandwidth for functions such as scan and select with the adaptive selection ratio, as well as system resource utilization and storage bandwidth for functions such as machine learning with the adaptive learning ratio. Based on the adaptive selection ratio, the learning ratio, or combination thereof, the adaptive block  1130  can control host computation and storage computation with an offload ratio. 
     In an embodiment, the adaptive block  1130 , the system device  1112 , the storage device  1114 , or combination thereof, can provide scheduling for distributing machine learning with the system device  1112 , the storage device  1114 , or combination thereof. One or more of the storage device  1114 , such as a smart solid-state storage device (SSD), can provide offloading machine learning, distributing machine learning, partitioning machine learning, or combination thereof. 
     In an embodiment, the electronic learning system  1100  can include or define functions including offloadable machine learning components or functions. The offloadable machine learning components or offloadable machine learning processes or machine learning functions can include main machine learning algorithms, filter, scan, preprocessing functions, any machine learning, or combination thereof. The electronic learning system  1100  can also divide, partition, select, offload, or combination thereof, the offloadable machine learning components or functions. The offloadable machine learning components or functions can be divided, partitioned, selected, or combination thereof, for one or more iterations of machine learning. The electronic learning system  1100  can provide monitoring for dynamic scheduling. 
     For illustrative purposes, the electronic learning system  1100  is shown with the adaptive block  1130  of the storage device  1114  although it is understood that the system device  1112  may also include an adaptive block  1130 , a selection block such as the selection block  1140 , functions, blocks for scan, blocks for filter, or combination thereof. The storage device  1114 , the system device  1112 , or combination thereof may also include any number or type of blocks. 
     In an embodiment, the adaptive block  1130  can process, select, partition, or combination thereof, data, such as training data, selected data, or combination thereof, including a first data block  1152 , a second data block  1154 , a third data block  1156 , or combination thereof. The adaptive block  1130  can process data received from the first data block  1152 , such as a training data block, and provide data for the second data block  1154 , such as a selected system data block, the third data block  1156 , such as a selected storage data block, or combination thereof. The second data block  1154 , the third data block  1156 , or combination thereof, can include an intelligently selected data block of the first data block  1152 , an intelligently updated data block of the first data block  1152 , a partial data block of the first data block  1152 , or combination thereof. 
     The first data block  1152 , the second data block  1154 , the third data block  1156 , or combination thereof, can provide selected data for parallel or distributed machine learning. The first data block  1152 , the second data block  1154 , the third data block  1156 , or combination thereof, can be implemented at least in part in hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or a combination thereof. 
     In an embodiment, the storage learning block  1124  can process, analyze, predict, recommend, filter, learn, receive, transmit, or combination thereof, data of the third data block  1156 . For example, the electronic learning system  1100  can provide prediction, recommendation, filtering, machine learning processes, machine learning functions, or combination thereof, based on clicking or selecting an advertisement or advertiser, recommending an item or items, filtering spam, or combination thereof. In some embodiments, prediction can be based on clicking or selecting an advertisement or advertiser, can recommend an item or items, can filter spam, can include like processes, or combination thereof, to provide parallel or distributed machine learning such as for big data analytics. Similarly, the system learning block  1128  can process, analyze, predict, recommend, filter, learn, receive, transmit, or combination thereof, data of the second data block  1154  for prediction, recommendation, filtering, machine learning processes, machine learning functions, or combination thereof. 
     In an embodiment, the system device  1112  of the electronic learning system  1100  can include a programming interface, such as the programming interface  1082  of  FIG. 10 , for parallel or distributed machine learning including machine learning processes outside the system device  1112 . The commands can apply to data, including big data, for all machine learning processes or algorithms including processing, scanning, filtering, analyzing, compressing, Karush-Kuhn-Tucker (KKT) filtering, Karush-Kuhn-Tucker (KKT) vector error filtering, random filtering, predicting, recommending, learning, or combination thereof. 
     In an embodiment, the system device  1112 , the storage device  1114 , or combination thereof, can identify issues including a bottleneck, resource saturation, pressure, limiting resource, or combination thereof. For example, the interface  1116  can include saturated input and output (I/O), which can provide reduced I/O for the electronic learning system  1100 . The system device  1112 , the storage device  1114 , or combination thereof, can identify the saturated input and output (I/O) of the interface  1116  and intelligently offload, throttle, parallelize, distribute, or combination thereof, processing, including functions, such as the functions  1040  of  FIG. 10 , on the storage control unit  1132 , the system control unit  1134 , or combination thereof. 
     In an embodiment, the adaptive block  1130 , the system device  1112 , the storage device  1114 , or combination thereof, with the intelligent offloading, throttling, paralleling, distributing, or combination thereof, can alleviate issues including the bottleneck, resource saturation, pressure, limiting resource, or combination thereof. Alleviating issues can provide significantly higher throughput for the electronic learning system  1100 , the electronic system  110 , or combination thereof. 
     In an embodiment, the system device  1112 , the storage device  1114 , or combination thereof, can include a scheduler block  1184 , which can include a high level programming language, for implementing the functions, scheduling algorithms, assigning functions, assigning data, or combination thereof. A scheduler block  1184  can include an ad-hoc style per-node scheduler and access a control unit of the storage device  1114 , such as the first storage control unit  132 , the second storage control unit  152 , or combination thereof, for selecting, partitioning, dividing, or combination thereof, parallel or distributed machine learning with a control unit of the system device  1112 , such as the first control unit  112 , the second control unit  134 , or combination thereof. The electronic learning system  1100  can provide SSD-runnable binaries for the adaptive block  1130  for offloading scan and filter, a main iterative algorithm, or combination thereof, based on an I/O bottleneck, such as saturated I/O, to alleviate pressure. 
     For example, the adaptive block  1130  can filter additional data including the first data  1152  based on determining a bottleneck such as an I/O bottleneck, saturated I/O, or combination thereof. The adaptive block  1130  can filter less data including the first data  1152  based on determining there is additional bandwidth including bandwidth in the interface  1116 . The electronic learning system  1100  with the scheduler block  1184  can determine offloading at least a portion of the main iterative algorithm if a further I/O bottleneck is detected after offloading scan and filter. Data can be retrieved by the system device  1112 , the storage device  1114 , or combination thereof, randomly, regularly, or combination thereof, for determining I/O bottlenecks. 
     In an embodiment in a manner similar to the electronic learning system  1000 , the system device  1112 , the storage device  1114 , or combination thereof, can also include a programming interface, such as the scheduler block  1084  of  FIG. 10  that can include a high level programming language, for implementing the commands, the functions, scheduling algorithms, assigning functions, assigning data, or combination thereof. 
     In an embodiment, the commands, the programming interface, or combination thereof, can include system calls such as new system calls, extended system calls, system status, system status of input and output bandwidth, system-status of computational utilization, system status of throttling, or combination thereof, for implementing the commands, the functions, or combination thereof. The commands, the programming interface, or combination thereof, can be based on system information  1186 . Further, the adaptive block  1130  can intelligently select, partition, or combination thereof, the first data  1152  based on the system information  1186 . 
     In an embodiment, the system information  1186  can include system utilization, system bandwidth, system parameters, input and output (I/O) utilization, memory utilization, memory access, storage utilization, storage access, control unit utilization, control unit access, central processing unit (CPU) utilization, CPU access, memory processor utilization, memory processor access, or combination thereof. The system information  1186  can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, the first storage media  126 , the second storage media  142 , or a combination thereof. 
     In an embodiment, the system device  1112 , the storage device  1114 , or combination thereof, can also communicate with a service interface, such as the service interface  1088  of  FIG. 10 , which can include a web service interface. The service interface can provide communication with services external to the electronic learning system  1100 , the electronic system  110 , or combination thereof. 
     In an embodiment, the storage device  1114  can include Flash, solid-state drives (SSD), phase-change memory (PCM), spin-transfer torque random access memory (STT-RAM), resistive random access memory (ReRAM), magentoresistive random access memory (MRAM), any storage device, any memory device, or combination thereof. Storage devices  1114  and system devices  1112  can connect with the interface  1116  including memory bus, serial attached small computer system interface (SAS), serial attached advanced technology attachment (SATA), non-volatile memory express (NVMe), Fiber channel, Ethernet, remote direct memory access (RDMA), any interface, or combination thereof. 
     For illustrative purposes, the electronic learning system  1100  is shown with one of each of the components although it is understood that any number of the components may be included. For example, the electronic learning system  1100  can include more than one of the storage device  1114 , the first data block  1152 , the second data block  1154 , the third data block  1156 , any component, or combination thereof. 
     It has been discovered that the electronic learning system  1100  with the adaptive block  1130 , the storage learning block  1124 , the system learning block  1128 , or combination thereof, can identify and alleviate issues with intelligent processing offload, throttling, paralleling, distribution, or combination thereof. The intelligent process offloading, throttling, paralleling, distributing, or combination thereof, can significantly improve performance by at least avoiding interface  1116  bottlenecks, resource saturation, pressure or combination thereof, utilizing higher bandwidth in the system device  1112 , the storage device  1114 , or combination thereof. 
     Referring now to  FIG. 12 , therein is shown a block diagram of a portion of an electronic learning system  1200  of the electronic system  100  in an embodiment of the invention. The electronic learning system  1200  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  1200  can control, place, or combination thereof, machine learning algorithms and can be included in the adaptive block  1130  of  FIG. 11  or the preprocessing block  1030  of  FIG. 10 . Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  1200  can include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  1200  can include adaptive processes for parallel or distributed machine learning such as scanning, filtering, predicting, recommending, machine learning processes, machine learning functions, big data analytics, or combination thereof. The adaptive processes can include control parameters, selection ratios, learning ratios, host computation to storage computation ratios, or combination thereof, for parallel or distributed machine learning and hardware control including system, in storage computing (ISC), solid-state storage device (SSD), or combination thereof. 
     For example, the electronic learning system  1200  can intelligently select or partition data, such as training data, for providing significantly improved partial data. The significantly improved partial data can improve updating a model, vector, or combination thereof. The intelligent selection or partitioning can provide faster convergence with increased input and output (I/O). 
     In an embodiment, the electronic learning system  1200  can include a learning block  1220  such as a machine learning block, an adaptive block  1230  such as an adaptive mechanism block, a load block  1250  such as an offload block, in storage computing (ISC), or combination thereof. The learning block  1220 , the adaptive block  1230 , the load block  1250 , or combination thereof, can be implemented at least in part as hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. For example, the learning block  1220  can provide learning rates that can be input into machine learning algorithms, machine learning blocks, the learning blocks  1120  of  FIG. 11 , the learning blocks  1020  of  FIG. 10 , the learning block  910  of  FIG. 9 , the learning block  810  of  FIG. 8 , the learning block  210  of  FIG. 2 , or combination thereof. 
     In an embodiment, the adaptive block  1230  can process, calculate, determine, or combination thereof, an adaptive select parameter  1252 , an adaptive learn parameter  1254 , an adaptive load parameter  1256 , or combination thereof. The adaptive select parameter  1252 , an adaptive learn parameter  1254 , an adaptive load parameter  1256 , or combination thereof, can be implemented at least in part in hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. For example, the adaptive block  1230  as an adaptive preprocessing block can intelligently process, calculate, determine, or combination thereof, parameters for partitioning data based on dynamic system behavior monitoring. 
     For example, the learning block  1220 , the adaptive block  1230 , the load block  1250 , or combination thereof, can be implemented at least in part with the first control unit  112 , the second control unit  134 , the first storage control unit  132 , the second storage control unit  152 , or combination thereof. The adaptive select parameter  1252 , the adaptive learn parameter  1254 , the adaptive load parameter  1256 , or combination thereof, can be implemented at least in part with the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or combination thereof. 
     In an embodiment, the adaptive block  1230  can process software control parameters including the adaptive select parameter  1252 , the adaptive learn parameter  1254 , or combination thereof, for a parallel or distributed machine learning process including a machine learning algorithm of the learn block  1220 , the preprocessing block  1240 , or combination thereof. The software control, such as the adaptive select parameter  1252 , the adaptive learn parameter  1254 , or combination thereof, can include control parameters of the machine learning algorithm, selection ratios, learning ratios, or combination thereof. For example, the preprocessing block  1240  can provide preprocessing functions such as scan, select, any machine learning processes, any machine learning function, or combination thereof, and can be based on a selection ratio. 
     In an embodiment, the adaptive block  1230  can process hardware control parameters including the adaptive load parameter  1256  for an offloading process of the load block  1250 , which can include offloading machine learning with parallel processing for in storage computing (ISC), smart solid-state storage device (SSD), or combination thereof. The hardware control, such as the adaptive load parameter  1256 , can control ratios of host computation to storage computation, offloading ratios, or combination thereof. 
     In an embodiment, the adaptive select parameter  1252 , the adaptive learn parameter  1254 , the adaptive load parameter  1256 , or combination thereof, can include a select ratio, a learn ratio, a load ratio, respectively, computed by the adaptive block  1230  based on system information  1280  including system utilization, system bandwidth, system parameters, input and output (I/O) utilization, memory utilization, memory access, storage utilization, storage access, control unit utilization, control unit access, central processing unit (CPU) utilization, CPU access, storage processor utilization, storage processor access, or combination thereof. The system information  1280  can be implemented in hardware such as integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, the first storage unit  114 , the second storage unit  146 , the first storage media  126 , the second storage media  142 , or a combination thereof. 
     For illustrative purposes, the electronic learning system  1200  is shown with one each of the adaptive block  1230 , the adaptive select parameter  1252 , the adaptive learn parameter  1254 , the adaptive load parameter  1256 , or combination thereof, although it is understood that the electronic learning system  1200  can include any number of the blocks such as the adaptive block  1230 , the adaptive select parameter  1252 , the adaptive learn parameter  1254 , the adaptive load parameter  1256 , or combination thereof. 
     It has been discovered that the electronic learning system  1200  with the system information  1280  provides parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors for faster convergence with increased input and output (I/O) based on the adaptive block  1230  including the adaptive select parameter  1252 , the adaptive learn parameter  1254 , the adaptive load parameter  1256 , or combination thereof. The adaptive block  1230  using system information  1280  can intelligently select or partition data, such as training data, for providing significantly improved partial data. 
     Referring now to  FIG. 13 , therein is shown a block diagram of a portion of an electronic learning system  1300  of the electronic system  100  in an embodiment of the invention. The electronic learning system  1300  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  1300  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  1300  can include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  1300  can include adaptive processes for machine learning such as scanning, filtering, predicting, recommending, machine learning processes, machine learning functions, big data analytics, or combination thereof. The adaptive processes can include control parameters, selection ratios, learning ratios, host computation to storage computation ratios, or combination thereof, for parallel or distributed machine learning and hardware control including system, in storage computing (ISC), solid-state storage device (SSD), or combination thereof. 
     For example, the electronic learning system  1300  can intelligently select or partition data, such as training data, for providing significantly improved partial data. The significantly improved partial data can improve updating a model, vector, or combination thereof. The intelligent selection or partitioning can provide faster convergence with increased input and output (I/O). 
     In an embodiment, the electronic learning system  1300  can include a first compute device  1312 , a second compute device  1314 , a third compute device  1316 , or combination thereof. The first compute device  1312 , the second compute device  1314 , the third compute device  1316 , or combination thereof, can be implemented as a system device or a storage device such as the system device  512  of  FIG. 5 , the storage device  514  of  FIG. 5 , the system device  912  of  FIG. 9 , the storage device  914  of  FIG. 9 , the system device  1012  of  FIG. 10 , the storage device  1014  of  FIG. 10 , the system device  1112  of  FIG. 11 , the storage device  1114  of  FIG. 11 , the first device  102 , the second device  106 , any compute device, or combination thereof. For illustrative purposes, the electronic learning system  1300  is shown with three compute devices although it is understood that any number and type of compute devices may be included. 
     In an embodiment, the electronic learning system  1300  can include a model device  1370  such as model server. The model device can be implemented as a system device, a storage device, or combination thereof. The model device  1370  can include but does not require computing resources. The model device  1370  can include machine learning data, vectors, parameters, models, or combination thereof, based on machine learning data, big data machine learning, analyzed big data, big data analytics, or combination thereof. 
     For illustrative purposes, the first compute device  1312 , the second compute device  1314 , the third compute device  1316 , the model server  1370 , or combination thereof, are shown as discrete blocks although it is understood that any of the blocks can share portions of the hardware with any of the other blocks. For example, the first compute device  1312 , the second compute device  1314 , the third compute device  1316 , the model server, or combination thereof, can share portions of hardware circuits or components. 
     The electronic learning system  1300  can include at least a vector of a first model  1372  such as a storage device computed model. The electronic learning system  1300  can also include at least a vector of a second model  1374  such as a host device or system device computed model. The electronic learning system  1300  can provide at least a vector of new or updated models for the first model  1372 , the second model  1374 , or combination thereof. The first model  1372 , the second model  1374 , or combination thereof, can be provided, stored, updated, amended, written, recorded, entered, revised, replaced, or combination thereof, on the model device  1370 . 
     For illustrative purposes, the first model  1372  is shown as a storage device computed model although it is understood that the first model  1372  can be computed with any device. Similarly, for illustrative purposes, the second model  1374 , is shown as a host or system computed model although it is understood that the second model  1374  can be computed with any device. 
     A scheduler  1380  can intelligently assign data, tasks, functions, operations, or combination thereof, for the first compute device  1312 , the second compute device  1314 , the third compute device  1316 , the model server  1370 , or combination thereof. The intelligent selection or partitioning of data, such as training data, can provide significantly improved partial data. The significantly improved partial data can improve updating a model, vector, or combination thereof. The intelligent selection or partitioning can provide faster convergence with increased input and output (I/O). 
     The scheduler  1380  can include a first scheduler block  1382 , a second scheduler block  1384 , a third scheduler block  1386 , or combination thereof, and can include a high level programming language, for implementing functions such as the function  1040  of  FIG. 10 , scheduling algorithms, assigning functions, assigning data, or combination thereof. The first scheduler block  1382 , the second scheduler block  1384 , the third scheduler block  1386 , or combination thereof, can include an ad-hoc style per-node scheduler and access control units of the first compute device  1312 , the second compute device  1314 , the third compute device  1316 , or combination thereof, for selecting, partitioning, dividing, machine learning, or combination thereof. 
     For illustrative purposes, the scheduler  1380  is shown with three blocks, such as the first scheduler block  1382 , the second scheduler block  1384 , the third scheduler block  1386 , or combination thereof, although it is understood that the scheduler  1380  may include any number of blocks. The scheduler  1380  can also function as a single block with multiple sub blocks such as the first scheduler block  1382 , the second scheduler block  1384 , the third scheduler block  1386 , or combination thereof. 
     It has been discovered that the electronic learning system  1300  with the scheduler  1380  can intelligently select, partition, divide, or combination thereof, parallel or distributed machine learning including big data analytics. The scheduler  1380  can intelligently offload, select, partition, divide, or combination thereof, data, processes, analyses, functions, learning, or combination thereof, for providing first models  1372  and second models  1374  with the first compute device  1312 , the second compute device  1314 , the third compute device  1316 , or combination thereof. 
     Referring now to  FIG. 14 , therein is shown a process flow of an electronic learning system  1400  of the electronic system  100  in an embodiment of the invention. The electronic learning system  1400  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  1400  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  1400  can include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  1400  can include adaptive processes for parallel or distributed machine learning such as scanning, filtering, predicting, recommending, machine learning processes, machine learning functions, big data analytics, or combination thereof. The adaptive processes can include control parameters, selection ratios, learning ratios, host computation to storage computation ratios, or combination thereof, for parallel or distributed machine learning and hardware control including system, in storage computing (ISC), solid-state storage device (SSD), or combination thereof. 
     For example, the electronic learning system  1400  can intelligently select or partition data, such as training data, for providing significantly improved partial data. The significantly improved partial data can improve updating a model, vector, or combination thereof. The intelligent selection or partitioning can provide faster convergence with increased input and output (I/O). 
     In an embodiment, the electronic learning system  1400  can include a start of process  1410 , a first detect process  1420 , an offload process  1430 , a monitor process  1440 , a second detect process  1450 , a throttle process  1460 , a check process  1470 , an end of process  1480 , or combination thereof. The process flow of the electronic learning system  1400  can include iterations of each process based on continued bottlenecks. For illustrative purposes, two detect processes are shown although it is understood that any number of detect process may be included. Further for illustrative purposes, one each of the other processes is shown although it is understood that any number or type of process may be included. 
     In an embodiment, the first detect process  1420  can detect issues such as bottlenecks, resource saturation, pressure, limited resources, or combination thereof, with the electronic system  100 . For example, the first detect process  1420  can detect input and output issues with a system or host device and a storage device. Based on detecting an issue, the first detect process  1420  can continue with, direct to, cancel, or invoke the offload process  1430 . Based on not detecting an issue, the first detect process  1420  can continue with, direct to, cancel, or invoke the monitor process  1440 . 
     In an embodiment, the offload process  1430  can provide intelligent processing including selected processing, partitioned processing, parallel processing, distributed processing, or combination thereof. The offload process  1430  can select, partition, parallelize, distribute, or combination thereof, machine learning including data, tasks, processes, functions, big data, analytics, or combination thereof. For example, the offload process  1430  can distribute data and processes for in storage computing (ISC) from a host or system. The offload process  1430  can continue with, direct to, or invoke the monitor process  1440 . 
     In an embodiment, the monitor process  1440  can provide detecting, identifying, monitoring, measuring, checking, or combination thereof, of the detected issues of the first detect process  1420 , such as bottlenecks, resource saturation, pressure, limited resources, or combination thereof. The monitor process  1440  can continue with, direct to, or invoke the second detect process  1450 . 
     In an embodiment, the second detect process  1450  can detect issues such as bottlenecks, resource saturation, pressure, limited resources, or combination thereof, with the electronic system  100 . For example, the second detect process  1450  can detect input and output issues with a system or host device and a storage device. Based on detecting an issue, the second detect process  1450  can continue with, direct to, or invoke the throttle process  1460 . Based on not detecting an issue, the second detect process  1450  can continue with, direct to, or invoke the check process  1470 . 
     In an embodiment, the throttle process  1460  can provide intelligent processing including selected processing, partitioned processing, parallel processing, distributed processing, or combination thereof. The throttle process  1460  can select, partition, parallel, distribute, or combination thereof, machine learning including data, tasks, processes, functions, big data, analytics, or combination thereof. For example, the throttle process  1460  can distribute data and processes for in storage computing (ISC) from a host or system. The throttle process  1460  can continue with, direct to, or invoke the check process  1470 . 
     In an embodiment, the check process  1470  can determine resolution, completion, end, or combination thereof, of an issue such as bottlenecks, resource saturation, pressure, limited resources, or combination thereof, with the electronic system  100 . Based on determining that the issue with the electronic system  100  is resolved, the check process  1470  can continue with, direct to, or invoke the end of process  1480 . Based on determining that the issue with the electronic system  100  is unresolved, the check process  1470  can continue with, direct to, or invoke the monitor process  1440 . 
     It has been discovered that the process flow of the electronic learning system  1400  including the first detect process  1420 , the offload process  1430 , the monitor process  1440 , the second detect process  1450 , the throttle process  1460 , the check process  1470 , or combination thereof, can include iterations until bottlenecks are resolved. The first detect process  1420 , the second detect process  1450 , the check process  1470 , or combination thereof, can iterate based on continued detection of unresolved bottlenecks. 
     Referring now to  FIG. 15 , therein is shown a block diagram of a portion of an electronic learning system  1500  of the electronic system  100  in an embodiment of the invention. The electronic learning system  1500  can be implemented with the first device  102  of  FIG. 1 , the second device  106  of  FIG. 1 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the electronic learning system  1500  can provide machine learning. Machine learning can include algorithms that can learn from data including artificial intelligence, getting computers to act without being explicitly programmed, automated reasoning, automated adaptation, automated decision making, automated learning, ability for a computer to learn without being explicitly programmed, artificial intelligence (AI), or combination thereof. Machine learning can be considered a type of artificial intelligence (AI). Machine learning can be implemented when designing and programming explicit, rule-based algorithms is infeasible. 
     In an embodiment, the electronic learning system  1500  provide machine learning including prediction, recommendation, filtering, machine learning processes, machine learning functions, or combination thereof. In some embodiments, prediction, for example, can be based on clicking or selecting an advertisement or advertiser, can recommend an item or items, can filter spam, can include like processes, or combination thereof, to provide parallel or distributed machine learning such as for big data analytics. 
     For example, the electronic learning system  1500  can include parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. The parallel or distributed machine learning for parallel processing can be implemented at least with the first control unit  112  of  FIG. 1 , the second control unit  134  of  FIG. 1 , the first storage unit  114  of  FIG. 1 , the second storage unit  146  of  FIG. 1 , the first storage control unit  132  of  FIG. 1 , the second storage control unit  152  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof. 
     In an embodiment, the electronic learning system  1500  can include a host device  1512  such as a system device, a storage device  1514 , or combination thereof. The host device  1512  and the storage device can be implemented with the first device  102 , any sub-components of the first device  102 , the second device  106 , any sub-components of the second device  106 , integrated circuits, integrated circuit cores, integrated circuit components, microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     In an embodiment, the host device  1512  can communicate with the storage device  1514  with an interface  1516 . The interface  1516  can include serial attached small computer system interface (SAS), serial attached advanced technology attachment (SATA), non-volatile memory express (NVMe), Fiber channel (FC), Ethernet, remote direct memory access (RDMA), InfiniBand (IB), any interface, or combination thereof. 
     In an embodiment, the storage device  1514  can include a learning engine  1524 , storage central processing unit (CPU)  1532 , or combination thereof. One or more of the storage CPUs  1524  can provide parallel or distributed machine learning, including cluster computing, for parallel processing in system processors as well as in storage computing (ISC) processors. For illustrative purposes, four of the storage CPUs  1532  are shown although it is understood that any number of storage CPUs may be included. 
     In an embodiment, the storage device  1514  can include memory  1542 , non-volatile memory  1546 , firmware  1548 , or combination thereof. The memory  1542  can include volatile memory such as dynamic random access memory (DRAM), static random access memory (SRAM), other memory technology, or combination thereof. The non-volatile memory  1546  can include flash drives, disk storage, non-volatile memory (NVM), other storage technology, or combination thereof. The firmware  1548  can be stored in a non-volatile storage device including read only memory (ROM), erasable programmable read only memory (EPROM), flash memory, other storage technology, or combination thereof. The memory  1542 , the non-volatile memory  1546 , the firmware  1548 , or combination thereof, can be implemented with integrated circuits, integrated circuit cores, integrated circuit components, passive devices, or a combination thereof. 
     In an embodiment, the memory  1542 , the non-volatile memory  1546 , the firmware  1548 , or combination thereof, can store relevant information for the storage device  1514 . The relevant information can include incoming data, previously presented data, temporary data, any form of social network day, user profiles, behavior data, cookies, any form of a large collection of user data, or a combination thereof, for operation of the storage device  1514 . 
     In an embodiment, the host device  1512  can include an application  1572  such as parallel or distributed machine learning applications, system calls  1582 , system commands  1584 , a driver  1586  such as a device driver, or combination thereof. The application  1572  can provide the system call  1582  including system status, input and output control (Ioctl), “LEARN” such as the “LEARN” command of  FIG. 10 , metadata, any system call, or combination thereof, for the driver  1586 . The driver  1586  can provide the system commands  1584  for the storage device  1514  based on the system calls  1582 . 
     For example, the system calls  1582 , the system commands  1584 , or combination thereof, can be based on detecting, identifying, monitoring, measuring, checking, or combination thereof, potential issues or bottlenecks with system resources. The issues or bottlenecks can include resource saturation, pressure, limited resource, or combination thereof, of any system resource including the interface  1516 . The detecting, identifying, monitoring, measuring, checking, or combination thereof, provides intelligence in implementing parallel or distributed processing for the learning engine  1524 , the system calls  1582 , the system commands  1584 , or combination thereof. 
     In an embodiment, the system commands  1584  of the driver  1586  can be divided, partitioned, selected, or combination thereof, for the learning engine  1524 , the storage CPU  1532 , the host  1512  or combination thereof. The firmware  1548  can divide, partition, select, or combination thereof, the system commands  1584  into regular commands  1592  for the storage CPU  1532  and learning commands  1594  for the learning engine  1524 . The learning commands  1594  such as machine learning commands, can provide parallel or distributed machine learning, including cluster computing, for parallel processing in the storage CPUs  1532 . The regular commands  1592  can include system commands  1594  for the storage device  1514  that do not require machine learning. 
     It has been discovered that the electronic learning system  1500  with the host device  1512 , the storage device  1514 , the system calls  1582 , the system commands  1584 , the learning engine  1524 , the storage CPU  1532 , or combination thereof, provide intelligent parallel or distributed machine learning, including cluster computing, for parallel processing. The system calls  1582 , the system commands  1584 , or combination thereof, provides the intelligence for parallel processing with the storage CPUs  1532 . 
     Referring now to  FIG. 16 , therein is shown examples of embodiments of the electronic system  100 . The example embodiments of the electronic system  100  with partitioning mechanism can include application examples for the electronic system  100  such as a smart phone  1612 , a dash board of an automobile  1622 , a notebook computer  1632 , a server  1642 , or combination thereof. These application examples illustrate purposes or functions of various embodiments of the invention and importance of improvements in processing performance including improved bandwidth, area-efficiency, or combination thereof. 
     For example, the storage device  1114  of  FIG. 11  can provide significantly improved system performance and avoid issues, bottlenecks, resource saturation, pressure, limited resource, or combination thereof, in the electronic system  100  such as a smart phone  1612 , a dash board of an automobile  1622 , a notebook computer  1632 , a server  1642 , or combination thereof. The adaptive block  1130  of  FIG. 11 , the scheduler block  1084  of  FIG. 10 , or combination thereof, of the storage device  1114  can intelligently select, partition, parallel, divide, or combination thereof, machine learning. 
     For example, the first compute device  1312  of  FIG. 13  can provide significantly improved system performance and avoid issues, bottlenecks, resource saturation, pressure, limited resource, or combination thereof, in the electronic system  100  such as a smart phone  1612 , a dash board of an automobile  1622 , a notebook computer  1632 , a server  1642 , or combination thereof. The scheduler  1380  of  FIG. 13  can intelligently select, partition, parallel, divide, or combination thereof, machine learning. 
     For example, the storage device  1514  of  FIG. 15  can provide significantly improved system performance and avoid issues, bottlenecks, resource saturation, pressure, limited resource, or combination thereof, in the electronic system  100  such as a smart phone  1612 , a dash board of an automobile  1622 , a notebook computer  1632 , a server  1642 , or combination thereof. The learning engine  1524  of  FIG. 15  of the storage device  1514  of  FIG. 15  can intelligently select, partition, parallel, divide, or combination thereof, machine learning. 
     In an example where an embodiment of the invention is an integrated physical logic circuit and the storage device  1114 , the storage device  1314  of  FIG. 13 , the storage device  1514 , or combination thereof, is integrated in the control unit  112  of  FIG. 1 , the storage unit  114  of  FIG. 1 , the first storage media  126  of  FIG. 1 , the second storage media  142  of  FIG. 1 , or combination thereof, selecting, partitioning, paralleling, dividing, or combination thereof, of machine learning can significantly improve system performance. Various embodiments of the invention provide selecting, partitioning, paralleling, dividing, or combination thereof, of machine learning thereby improving system performance, improving energy efficiency, enabling new technologies, enabling compatibility with current hierarchies, providing transparent implementation for user applications, or combination thereof. 
     The electronic system  100 , such as the smart phone  1612 , the dash board of the automobile  1622 , the notebook computer  1632 , and the server  1642 , can include a one or more of a subsystem (not shown), such as a printed circuit board having various embodiments of the invention, or an electronic assembly (not shown) having various embodiments of the invention. The electronic system  100  can also be implemented as an adapter card in the smart phone  1612 , the dash board of the automobile  1622 , the notebook computer  1632 , and the server  1642 . 
     Thus, the smart phone  1612 , the dash board of the automobile  1622 , the notebook computer  1632 , the server  1642 , other electronic devices, or combination thereof, can provide significantly faster throughput with the electronic system  100  such as processing, output, transmission, storage, communication, display, other electronic functions, or combination thereof. For illustrative purposes, the smart phone  1612 , the dash board of the automobile  1622 , the notebook computer  1632 , the server  1642 , other electronic devices, or combination thereof, are shown although it is understood that the electronic system  100  can be used in any electronic device. 
     Referring now to  FIG. 17 , therein is shown a flow chart of a method  1700  of operation of the electronic system  100  in an embodiment of the present invention. The method  1700  includes: receiving system information with a storage interface in a block  1702 ; partitioning data, with a storage control unit configured to implement a preprocessing block, based on the system information in a block  1704 ; and distributing machine learning for processing partial data of the data, with the storage control unit configured to implement a learning block in a block  1706 . 
     In an embodiment, the method  1700  for the block  1704  can include the storage control unit configured to implement a preprocessing block. The method  1700  for the block  1704  can include the storage control unit configured to implement a programming interface. 
     In an embodiment the method  1700  for the block  1704  can include the storage unit configured to implement a selection block. The method  1700  for the block  1704  can include the storage control unit configured to implement a scan and select block. 
     The method  1700  for the block  1706  can include the storage control unit configured to implement a scheduler block. 
     The method  1700  can further include storing data with a storage media configured to store the data. 
     The resulting method, process, apparatus, device, product, and/or system is straightforward, cost-effective, uncomplicated, highly versatile, accurate, sensitive, and effective, and can be implemented by adapting known components for ready, efficient, and economical manufacturing, application, and utilization. Another important aspect of an embodiment of the present invention is that it valuably supports and services the historical trend of reducing costs, simplifying systems, and increasing performance. These and other valuable aspects of an embodiment of the present invention consequently further the state of the technology to at least the next level. 
     While the invention has been described in conjunction with a specific best mode, it is to be understood that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the aforegoing description. Accordingly, it is intended to embrace all such alternatives, modifications, and variations that fall within the scope of the included claims. All matters set forth herein or shown in the accompanying drawings are to be interpreted in an illustrative and non-limiting sense.