Patent Publication Number: US-2022214923-A1

Title: Memory system workload allocation

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to semiconductor memory and methods, and more particularly, to apparatuses, systems, and methods for memory system workload allocation. 
     BACKGROUND 
     Memory devices are typically provided as internal, semiconductor, integrated circuits in computers or other electronic systems. There are many different types of memory including volatile and non-volatile memory. Volatile memory can require power to maintain its data (e.g., host data, error data, etc.) and includes random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), synchronous dynamic random access memory (SDRAM), and thyristor random access memory (TRAM), among others. Non-volatile memory can provide persistent data by retaining stored data when not powered and can include NAND flash memory, NOR flash memory, and resistance variable memory such as phase change random access memory (PCRAM), resistive random access memory (RRAM), and magnetoresistive random access memory (MRAM), such as spin torque transfer random access memory (STT RAM), among others. 
     Memory devices may be coupled to a host (e.g., a host computing device) to store data, commands, and/or instructions for use by the host while the computer or electronic system is operating. For example, data, commands, and/or instructions can be transferred between the host and the memory device(s) during operation of a computing or other electronic system. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram in the form of an apparatus including a host and a memory device in accordance with a number of embodiments of the present disclosure. 
         FIG. 2  is another functional block diagram in the form of a computing system including an apparatus including a host and a memory system in accordance with a number of embodiments of the present disclosure. 
         FIG. 3  is a functional block diagram in the form of an apparatus including a memory system in accordance with a number of embodiments of the present disclosure. 
         FIG. 4  is another functional block diagram in the form of an apparatus including a memory system in accordance with a number of embodiments of the present disclosure. 
         FIG. 5  is a diagram illustrating a human medical self-diagnostic test subject and a mobile computing device in accordance with a number of embodiments of the present disclosure. 
         FIG. 6  is a flow diagram representing an example method corresponding to memory system workload allocation in accordance with a number of embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Methods, systems, and apparatuses related to a memory system workload allocation are described. For example, data corresponding to execution of workloads executed within a memory system can be selectively written to different types of memory within the memory system. A method includes receiving, by a processing unit coupled to a first memory device comprising a first type of media and a second memory device comprising a second type of media, data captured from an imaging device coupled to the processing unit and determining, by the processing unit, characteristics of a workload corresponding to processing of the data. The method further includes writing, by the processing unit, a portion of data associated with the workload to the other of the first memory device or the second memory device based on the determined characteristics of the workload and causing the workload to be executed while at least the portion of the data associated with the workload is written to the other of the first memory device or the second memory device. 
     As broadband cellular network technology evolves, higher resource demands may be placed on devices connected to a broadband cellular network. This can be due to increases in available bandwidth associated with broadband cellular networks (referred to herein for brevity as “networks”), which can, in turn, give rise to higher download speeds and therefore increased data traffic associated with devices connected to the network. Such increased data traffic can further give rise a greater quantity of data be received, stored, and/or processed within devices connected to the network. 
     In addition, the potential for increased data traffic involving devices, such as mobile computing devices, connected to the network can allow for increasingly complicated applications (e.g., computing applications that are designed to cause a computing device to perform one or more specific functions or tasks) to be executed on the devices. Execution of such applications can in turn give rise to demanding workloads, which can strain computing resources and, more specifically, strain computing resources that are allocated in some conventional approaches. 
     As used herein, the term “application” generally refers to one or more computer programs that can include computing instructions that are executable to cause a computing system to perform certain tasks, functions, and/or activities. An amount of computing resources (e.g., processing resources and/or memory resources) consumed in execution of an application can be measured in terms of a “workload.” As used herein, the term “workload” generally refers to the aggregate computing resources consumed in execution of applications that perform a certain task, function, and/or activity. During the course of executing an application, multiple sub-applications, sub-routines, etc. may be executed by the computing system. The amount of computing resources consumed in executing the application (including the sub-applications, sub-routines, etc.) can be referred to as the workload. 
     Some applications that can give rise to demanding workloads include applications that process data, such as images and/or video, in real time. Such applications, especially when processing of high-quality images and/or video in real time to correct imperfections in images and/or video are requested, can request usage of a large quantity of computing resources, and therefore create a demanding workload. Some examples of these kinds of applications can include medical diagnostic imaging applications, which can include examination of particular parts of the human body that are captured with images and/or video in real time and processed to diagnose ailments such as cataracts, cancerous cells, muscular system injuries, and/or glandular abnormalities, among others. 
     As workloads become increasingly demanding, especially in light of improvements to broadband cellular network technology, issues associated with optimization of workload handling can become further exacerbated in mobile computing devices (e.g., smartphones, tablets, phablets, and/or Internet-of-Things (IoT) devices, among others) where physical space constraints can dictate the amount of processing resources and/or memory resources available to the device. In addition, execution of demanding workloads using mobile computing devices can, in some approaches, quickly drain battery resources available to the mobile computing device and/or cause unwanted thermal behavior (e.g., the mobile computing device can become too hot to operate in a stable manner, etc.) for the mobile computing device. As used herein, the term “mobile computing device” generally refers to a handheld computing device that has a slate or phablet form factor. In general, a slate form factor can include a display screen that is between approximately 3 inches and 5.2 inches (measured diagonally), while a phablet form factor can include a display screen that is between approximately 5.2 inches and 7 inches (measured diagonally). Examples of “mobile computing devices” are not so limited, however, and in some embodiments, a “mobile computing device” can refer to IoT device, among other types of edge computing devices. 
     In order to attempt to execute demanding workloads on mobile computing devices, some approaches can include throttling performance of the mobile computing device during execution of some kinds of workloads to ensure sufficient computing resources are available to execute demanding workloads. In addition, some approaches can include throttling performance of the mobile computing device during execution of some kinds of workloads in an attempt to mitigate adverse effects on battery consumption and/or thermal behavior. However, such approaches may therefore only use a subset of the available computing resources and/or may not be able to take advantage of the available computing resources. This can be especially problematic in mobile computing devices which, as mentioned above may already feature diminished computing resources due to space constraints in comparison with, for example, a desktop computing device. 
     In contrast, embodiments described herein can provide hardware circuitry (e.g., a controller, processing unit, etc.) that can monitor and/or determine characteristics of workloads executed in a computing system or mobile computing device when data corresponding to the workloads is stored in different types of memory devices. The hardware circuitry can, based on the monitored or determined characteristics of the workloads, write at least a portion of the workload to a different type of memory device. For example, if the workload is executed while the data corresponding to the workload is stored in a volatile memory device and the hardware circuitry determines that execution of the workload can be optimized if the data corresponding to the workload is stored in a non-volatile memory device, the hardware circuitry can cause at least a portion of the data corresponding to the workload to be written to the non-volatile memory device. Such dynamic determination of workload characteristics and subsequent allocation of workloads to memory devices that include different types of media can be especially beneficial in mobile computing systems, especially as increasingly processing resource intensive workloads are executed on mobile computing devices. 
     Non-limiting examples of how the workload can be optimized can include optimizing battery consumption of the computing system, bandwidth associated with the computing system, computing resource consumption associated with the computing system, and/or speed of execution of the workload by the computing system, among others. For example, if the computing system is a mobile computing device (e.g. a smartphone, IoT device, etc.), battery power of the computing device may be rapidly depleted when the workload is executed involving certain types of high power consumption memory devices. Accordingly, in order to optimize battery power consumption, for example of a mobile computing device, the hardware circuitry can cause at least a portion of the data corresponding to the workload to be written to a memory device that is characterized by a lower power consumption in executing the workload. 
     Another non-limiting example of the workload can be optimized can include optimizing execution of the workload by utilizing memory devices and/or media types that exhibit different memory capacities versus bandwidth capabilities. For example, a memory device that exhibits high capacity but low bandwidth (e.g., a NAND memory device) can be utilized for execution of some types of workloads (or portions thereof) while a memory device that exhibits high bandwidth but low capacity (e.g., a 3D stacked SDRAM memory device) can be utilized for execution of some types of workloads (or portions thereof). By leveraging the capacity of a memory device that exhibits high capacity but low bandwidth, or vice versa, for differing workloads, embodiments herein can optimize an amount of time, processing resources, and/or power consumed in executing resource intensive applications in a computing device or mobile computing device. Embodiments are not so limited, however, and other examples of optimizing execution of the workload in accordance with the disclosure are described in more detail, herein. 
     As described in more detail, herein, embodiments can further optimize execution of workloads in mobile computing system by writing data associated with the workloads to the memory devices based on characteristics of that data such as access frequencies of data involved in execution of the workloads. Access frequency of the data can refer to a quantity of accesses (e.g., reads, writes, etc.) involving the data in execution of the workloads. Access frequency of the data can be referred to herein in terms of “hot data” and “cold data.” “Cold data,” as used herein, means that a particular memory object has not been accessed for a long duration relative to other memory objects read from a memory device. “Hot data,” as used herein, means that a particular memory object has been accessed frequently relative to other memory objects read from a memory device. 
     For example, if certain data involved in execution of a workload is determined to be “hot,” such data can be written to a memory device that includes a media type that is well suited for making data quickly accessible. A non-limiting example of a memory device to which hot data can be written during execution of the workloads described herein is a volatile memory device such as a DRAM device. 
     In contrast, if certain data involved in execution of a workload is determined to be “cold,” such data can be written to a memory device that includes a media type that is well suited for storing data that is not frequently accessed. A non-limiting example of a memory device to which cold data can be written during execution of the workloads described herein is a non-volatile memory device such as a NAND flash device. 
     In the following detailed description of the present disclosure, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration how one or more embodiments of the disclosure may be practiced. These embodiments are described in sufficient detail to enable those of ordinary skill in the art to practice the embodiments of this disclosure, and it is to be understood that other embodiments may be utilized and that process, electrical, and structural changes may be made without departing from the scope of the present disclosure. 
     As used herein, designators such as “N,” “M,” etc., particularly with respect to reference numerals in the drawings, indicate that a number of the particular feature so designated can be included. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” can include both singular and plural referents, unless the context clearly dictates otherwise. In addition, “a number of,” “at least one,” and “one or more” (e.g., a number of memory banks) can refer to one or more memory banks, whereas a “plurality of” is intended to refer to more than one of such things. 
     Furthermore, the words “can” and “may” are used throughout this application in a permissive sense (i.e., having the potential to, being able to), not in a mandatory sense (i.e., must). The term “include,” and derivations thereof, means “including, but not limited to.” The terms “coupled” and “coupling” mean to be directly or indirectly connected physically or for access to and movement (transmission) of commands and/or data, as appropriate to the context. The terms “data” and “data values” are used interchangeably herein and can have the same meaning, as appropriate to the context. 
     The figures herein follow a numbering convention in which the first digit or digits correspond to the figure number and the remaining digits identify an element or component in the figure. Similar elements or components between different figures may be identified by the use of similar digits. For example,  104  may reference element “ 04 ” in  FIG. 1 , and a similar element may be referenced as  204  in  FIG. 2 . A group or plurality of similar elements or components may generally be referred to herein with a single element number. For example, a plurality of reference elements, e.g., elements  126 - 1  to  126 -N (or, in the alternative,  126 - 1 , . . .  126 -N) may be referred to generally as  126 . As will be appreciated, elements shown in the various embodiments herein can be added, exchanged, and/or eliminated so as to provide a number of additional embodiments of the present disclosure. In addition, the proportion and/or the relative scale of the elements provided in the figures are intended to illustrate certain embodiments of the present disclosure and should not be taken in a limiting sense. 
       FIG. 1  is a functional block diagram in the form of a computing system  100  including an apparatus including a host  102  and a memory system  104  in accordance with a number of embodiments of the present disclosure. As used herein, an “apparatus” can refer to, but is not limited to, any of a variety of structures or combinations of structures, such as a circuit or circuitry, a die or dice, a module or modules, a device or devices, or a system or systems, for example. In some embodiments, the computing system  100  can be a mobile computing system (e.g., a mobile computing device, such as the mobile computing device  501  illustrated in  FIG. 5 , which can be a smartphone, a tablet, a phablet, and/or a IoT device, among others). The memory system  104  can include a number of different memory devices  123 ,  125  (and/or  227  illustrated in  FIG. 2 , herein), which can include one or more different media types  123 ,  125  (and/or  227  illustrated in  FIG. 2 , herein). The different memory devices  123 ,  125 , and/or  227  can include one or more memory modules (e.g., single in-line memory modules, dual in-line memory modules, etc.). 
     The memory system  104  can include volatile memory and/or non-volatile memory. In a number of embodiments, memory system  104  can include a multi-chip device. A multi-chip device can include a number of different memory devices  123 ,  125 , and/or  227 , which can include a number of different memory types and/or memory modules. For example, a memory system can include non-volatile or volatile memory on any type of a module. As shown in  FIG. 1 , the computing system  100  can include a controller  120 , which can include a processing unit  122 . Each of the components (e.g., the host  102 , the controller  120 , the processing unit  122 , and/or the memory devices  123 ,  125  can be separately referred to herein as an “apparatus.” 
     The memory system  104  can provide main memory for the computing system  100  or could be used as additional memory and/or storage throughout the computing system  100 . The memory system  104  can include one or more memory devices  123 ,  125 , which can include volatile and/or non-volatile memory cells. At least one of the memory devices  123 ,  125  can be a flash array with a NAND architecture, for example. Further, at least one of the memory devices  123 ,  125  can be a dynamic random-access array of memory cells. Embodiments are not limited to a particular type of memory device. For instance, the memory system  104  can include RAM, ROM, DRAM, SDRAM, PCRAM, RRAM, and/or flash memory (e.g., NAND and/or NOR flash memory devices), among others. 
     Embodiments are not so limited, however, and the memory system  104  can include other non-volatile memory devices  123 ,  125  such as non-volatile random-access memory devices (e.g., NVRAM, ReRAM, FeRAM, MRAM, PCM), “emerging” memory devices such as resistance variable (e.g., 3-D Crosspoint (3D XP)) memory devices, memory devices that include an array of self-selecting memory (SSM) cells, etc., or any combination thereof. 
     Resistance variable memory devices can perform bit storage based on a change of bulk resistance, in conjunction with a stackable cross-gridded data access array. Additionally, in contrast to many flash-based memories, resistance variable non-volatile memory can perform a write in-place operation, where a non-volatile memory cell can be programmed without the non-volatile memory cell being previously erased. In contrast to flash-based memories and resistance variable memories, self-selecting memory cells can include memory cells that have a single chalcogenide material that serves as both the switch and storage element for the memory cell. 
     As shown in  FIG. 1 , the memory devices  123 ,  125  include different types of memory devices. For example, the memory device  125  can be a 3D XP memory device or a NAND memory device, among others, and the memory device  123  can be a volatile memory device, such as a DRAM device, or vice versa. That is, the memory devices  123 ,  125  can include different media types  124 ,  126 . Embodiments are not so limited, however, and the memory devices  123 ,  125  can include any type of memory devices provided that at least two of the memory devices  123 ,  125  include different media types  124 ,  126 . As used herein, a “media type” generally refers to a type of memory cell architecture that corresponds to the memory devices  123 ,  125 . For example, one of the media types  124 ,  126  can correspond to an array of memory cells that include at least one capacitor and at least one transistor, while another of the media types  124 ,  126  can include an array of floating-gate metal-oxide-semiconductor field-effect transistors. In some embodiments, at least one of the media types  124 ,  126  can include an array of resistance variable memory cells that are configured to perform bit storage based on a change in a bulk resistance associated with the resistance variable memory cells. 
     As illustrated in  FIG. 1 , a host  102  can be coupled to the memory system  104 . In a number of embodiments, the memory system  104  can be coupled to the host  102  via one or more channels (e.g., channel  103 ). In  FIG. 1 , the memory system  104  is coupled to the host  102  via channel  103 , which can, in addition, be coupled to the controller  120  and/or the processing unit  122  of the memory system  104 . The controller  120  and/or the processing unit  122  are coupled to the memory devices  123 ,  125  via channel(s)  105 ,  107 . In some embodiments, each of the memory devices  123 ,  125  are coupled to the controller  120  and/or the processing unit  122  by one or more respective channels  105 ,  107  such that each of the memory devices  123 ,  125  can receive messages, commands, requests, protocols, or other signaling that is compliant with the type of memory device  123 ,  125  (e.g., messages, commands, requests, protocols, or other signaling that is compliant with the media type  124 ,  126  of the memory devices  123 ,  125 ) coupled to the controller  120 . 
     The computing system  100  can further include an imaging device  121 . The imaging device  121  can be communicatively coupled to the host  102  and/or to the memory device  104  (e.g., to the controller  120  and/or the processing unit  122 ). The imaging device  121  can be a camera, sonography device, ultrasound device, stereoscopic imaging device, magnetic resonance imaging device, infrared imaging device, or other imaging device that can capture data that includes images or streams of images (e.g., streaming video and/or “live-streaming video”) in real-time and transmit information corresponding to the images and/or streams of images to the computing system  100 . In general, the imagining device can be any mechanical, digital, or electronic viewing device; still camera; camcorder; motion picture camera; or any other instrument, equipment, or format capable of recording, storing, or transmitting images, video, and/or information. 
     As used herein, the term “live-streaming video,” and variants thereof, generally refers to sequences of images that are concurrently (or nearly concurrently) captured and processed, reproduced, and/or broadcasted. In some embodiments, “live-streaming” video can be referred to in the alternative herein as “data captured by an imagining device” or “data captured from an imagining device.” Further, as used herein, the term “streaming video,” and variants thereof generally refers to sequences of images that are captured by an imaging device and subsequently processed, reproduced, and/or broadcasted. In some embodiments, “streaming” video can be referred to in the alternative herein as “data captured by an imagining device” or “data captured from an imagining device.” 
     Generally, such data (e.g., images, streams of images and/or or “live-streaming” video) captured by the imaging device can be displayed or broadcast on a viewing device and/or processed by a processing unit within a threshold period of time after capture by the imagining device. In some embodiments, the data captured by the imaging device can be displayed, broadcast, and/or processed within a threshold period of time relative to capture of the imaging device that is on the order of seconds or minutes, as opposed to hours or days. These data (e.g., streams of images and/or video) can include any media content live or recorded that is delivered to or by a computing system, such as a mobile computing device, via a connection path, such as a wired communication channel, and/or a non-wired communication channel such as the internet and displayed or broadcast in real time. Accordingly, as described in more detail herein, data can be captured by an imaging device and then stored in memory coupled to the imaging device, processed by a processing unit associated with the memory device, and subsequently broadcast and/or the data can be captured by the imagining device, stored in memory coupled to the imaging device, processed by a processing unit associated with the memory device, and/or broadcast in real-time (or near real-time based on latencies in transmission between various components described herein) as the data is captured by the imaging device. 
     In some embodiments, the imaging device  121  can capture data, such as images and/or streaming video (e.g., live-streaming video) that includes images used in a medical self-diagnostic test. As used herein, a “medical self-diagnostic test” generally refers to medical testing performed by a patient from a location different than a doctor&#39;s office, clinic, hospital, or other health care service location. In general, a medical self-diagnostic test is performed by a patient using equipment that is owned by the patient and is commonly not medical grade equipment. For example, embodiments herein described the use of a smartphone or other mobile computing device in performance of a medical self-diagnostic test. 
     In some embodiments, the images and/or steaming video captured by the imaging device  121  can include images and/or streaming video of an eyeball, an ear canal, a nasal passage, a uterus, and/or a testicle, among others. Such images and/or streaming video can be captured by the imaging device  121  and processed locally within the memory system  104  as part of a medical self-diagnostic test. By utilizing such aspects of the disclosure, medical self-diagnostic tests can be performed in the absence of a visit to a doctor or hospital, which can alleviate wait times for medical patients and/or can preemptively capture medical information for a medical professional to view at a later time. In addition, such medical self-diagnostic tests can provide information over time in the absence of doctor office visits that can be amalgamated over time to assist in early detection of medical issues and/or to generate a consistent record of medical abnormalities that can later be analyzed by a doctor or other clinical professional. 
     For example, ultrasound images and/or video of a fetus can be captured by the imaging device  121  and processed by the memory system  104  to ensure healthy growth of the fetus and/or to detect and identify potential abnormalities in the fetus during a pregnancy in the absence of visits to a doctors&#39; office or hospital. Further, early signs of testicular abnormalities (e.g., signs of testicular cancer or other ailments involving the testes) and/or ocular abnormalities (e.g., cataracts or other ailments involving the eyes) can be captured by the imaging device  121  and processed by the memory system  104  to assist with early detection of such abnormalities in the absence of visits to a doctors&#39; office or hospital. Other examples of potential abnormalities that can be uncovered in accordance with the disclosure include abnormalities of the ears, nose, throat, glands, joints, and/or muscles, among others. 
     Traditionally, capture and processing/analysis of such medical abnormalities is a highly specialized and computing resource intensive process. For example, applications and hence, the workloads corresponding thereto, to perform medical imaging and/or process medical imaging data can be extremely computing resource intensive. One reason for this is that the level of detail captured in images or video for medical imaging purposes is often times extremely detailed and therefore memory resource intensive (e.g., because of the detail captured in such images and/or videos, the file sizes corresponding to the images and/or videos can be relatively large in comparison to, for example, a simple photograph). Another reason for the resource intensive nature of execution of applications and corresponding workloads to process medical imaging data is that the detail and size of the data (e.g., the file sizes associated with medical imaging data) can require multiple resource intensive operations in processing. 
     However, embodiments herein can allow for selective processing of workloads involving images and/or video corresponding to the images and/or video captured by the imaging device  121  such that the workloads corresponding to execution of applications involving the same are allocated to the memory devices  123 ,  125 ,  227  to optimize the performance of the memory system  104  such that the medical self-diagnostic tests described herein can be realized using a mobile computing device, such as a smartphone, among other mobile computing devices described herein. 
     In some embodiments, the images and/or video captured by the imaging device  121  and processed by the memory system  104  can be uploaded or otherwise transferred to a medical professional to assist in building long term records of the development of potential medical abnormalities and providing notifications of these records to a medical professional even if a patient is remiss in visiting a doctor or hospital regularly. 
     The host  102  can be a host system such as a personal laptop computer, a desktop computer, a digital camera, a smart phone, a memory card reader, and/or an internet-of-things (IoT) enabled device, among various other types of hosts. In some embodiments, however, the host  102  is a mobile computing device such as a digital camera, a smart phone, a memory card reader, and/or an internet-of-things (IoT) enabled device, among various other types of hosts (e.g., in some embodiments, the host  102  is not a personal laptop computer or desktop computer). The host  102  can include a system motherboard and/or backplane and can include a memory access device, e.g., a processor (or processing device). 
     One of ordinary skill in the art will appreciate that “a processor” can intend one or more processors, such as a parallel processing system, a number of coprocessors, etc. The system  100  can include separate integrated circuits or one or more of the host  102 , the memory system  104 , the control circuitry  120 , and/or the memory devices  126 - 1  to  126 -N can be on the same integrated circuit. The computing system  100  can be, for instance, a server system and/or a high-performance computing (HPC) system and/or a portion thereof. Although the example shown in  FIG. 1  illustrate a system having a Von Neumann architecture, embodiments of the present disclosure can be implemented in non-Von Neumann architectures, which may not include one or more components (e.g., CPU, ALU, etc.) often associated with a Von Neumann architecture. 
     The memory system  104  can include a controller  120 , which can include a processing unit  122 . The processing unit  122  can be provided in the form of an integrated circuit, such as an application-specific integrated circuit (ASIC), field programmable gate array (FPGA), reduced instruction set computing device (RISC), advanced RISC machine, system-on-a-chip, or other combination of hardware and/or circuitry that is configured to perform operations described in more detail, herein. In some embodiments, the processing unit  122  can comprise one or more processors (e.g., processing device(s), co-processors, etc.) 
     The processing unit  122  can perform operations to monitor and/or determine characteristics of workloads running on the memory system  104 . The characteristics can include information such as bandwidth consumption, memory resource consumption, access frequency (e.g., whether the data is hot or cold), and/or power consumption in execution of the workloads, among others. The processing unit  122  can control writing of at least a portion of the data to a different memory device  123 ,  125  in order to optimize execution of the workload, balance the workload between different memory devices  123 ,  125  for media management purposes, and/or optimize battery consumption of the computing system  100 , among others. 
     In a non-limiting example, an apparatus (e.g., the computing system  100 ) can include a first memory device  123  comprising a first type of media  124  and a second memory device  125  comprising a second type of media  126 . The first memory device  123 , the second memory device  125 , and the processing unit  122  can, in some embodiments, be resident on a mobile computing device (e.g., the mobile computing device  501  illustrated in  FIG. 5 , herein) such as a smartphone. A processing unit  122  can be coupled to the first memory device  123  and the second memory device  125 . The processing unit  122  can receive information captured by an imaging device  121  couplable to the processing unit  122 . 
     As used herein, the term “resident on” refers to something that is physically located on a particular component. For example, the first memory device  123 , the second memory device  125 , and/or the processing unit  122  can, in some embodiments, being resident on a smartphone (e.g., the computing device  100  and/or the mobile computing device  501  illustrated in  FIG. 5 , herein) refers to a condition in which the first memory device  123 , the second memory device  125 , and/or the processing unit  122  is physically coupled to, or physically within, smartphone (e.g., the computing device  100  and/or the mobile computing device  501  illustrated in  FIG. 5 , herein). The term “resident on” may be used interchangeably with other terms such as “deployed on” or “located on,” herein. 
     The processing unit  122  can execute an operation to process the received information captured by the imaging device  121 . In some embodiments, the operation to process received information captured by the imaging device  121  can involve an application having a particular workload corresponding thereto. The processing unit  122  can determine characteristics of the workload when the workload is written to the first memory device  123  or the second memory device  125 . In some embodiments, the characteristics of the workload can include at least one of an access frequency of data associated with the workload, a latency associated with execution of the workload, and/or an amount of processing resources consumed in execution of the workload. In some embodiments, the application and/or the workload can involve processing of data received and/or captured by the imaging device  121 . 
     The processing unit  122  can determine, based on the characteristics of the workload, whether to write at least a portion of data associated with the workload to the other of the first memory device  123  or the second memory device  125  and control allocation of execution of the workload that is written to the other of the first memory device  123  or the second memory device  125  such that at least the portion of the workload is subsequently executed after at least the portion of the workload has been written to the other of the first memory device  123  or the second memory device  125 . In some embodiments, the subsequently executed workload can involve processing of data received and/or captured by the imaging device  121 . 
     In some embodiments, the processing unit  122  can determine that the workload corresponds to performance of an ultrasound imaging operation, as described in more detail in connection with  FIG. 5 , herein. The processing unit  122  can then receive, from the imaging device  121 , information that corresponds to the ultrasound imaging operation and write the information that corresponds to the ultrasound imaging operation to the first memory device  123  or the second memory device  125  based, at least in part on the determination that the workload corresponds to performance of the ultrasound imaging operation. In some embodiments, the information that corresponds to the ultrasound imaging operation can be received and/or captured by the imaging device  121 . 
     Continuing with the above non-limiting example, the processing unit  122  can determine that the workload corresponds to performance of an operation to detect an abnormality in at least one of an eyeball, an ear, a nose, or a testicle, or any combination thereof, as described in more detail in connection with  FIG. 5 , herein. The processing unit  122  can then receive, from the imaging device  121 , information that corresponds to the detected abnormality in at least the one the eyeball, the ear, the nose, and/or the testicle and write at least the portion of the data associated with the workload to the other of the first memory device  123  or the second memory device  125  based, at least in part, on the determination that the workload corresponds to performance of the operation to detect the abnormality in at least one of the eyeball, the ear, the nose, or the testicle. 
     In some embodiments, the processing unit  122  can determine that the workload corresponds to performance of an operation to process an image or a video received from the imaging device  121 . The processing unit  122  can then write at least the portion of the data associated with the workload to the other of the first memory device  123  or the second memory device  125  based, at least in part, on the determination that the workload corresponds to performance of the operation to process the image or the video received from the imaging device  121  and cause performance of the operation to process the image or the video by replacing at least one pixel of the image or the video, correcting a blurred portion of the image or the video, or removing noise from the image and/or the video. For example, in the process of image capture, one or more pixels of an image or video may become corrupted, which can cause the image to be distorted, blurred, or include other types of noise. By performing operations to replace the corrupted portions (e.g., pixels) of the image, the image or video quality can be recovered and/or improved using circuitry that is entirely resident on the memory system (e.g., in the absence of transferring the images and/or video to external circuitry, such as the host  102 ). In some embodiments, the image and/or the video can be received from the imaging device  121  and processed in a live-streaming manner. For example, the video can be a live video captured in real-time by the imaging device  121  and written in real time to the memory system  104 . 
     As mentioned above, the first memory device  123  or the second memory device  125  can be a non-persistent (e.g., volatile) memory device, and the other of the first memory device  123  or the second memory device  125  can be a persistent (e.g., non-volatile) memory device. In addition, as mentioned above, in some embodiments, the first type of memory or the second type of memory, or both, comprises sets of memory cells that exhibit different storage characteristics. For example, the first memory device  123  can have a first media type  124  and the second memory device  125  can have a second media type  126  associated therewith. 
     Continuing with the above non-limiting example, the first memory device  123  or the second memory device  125  can be a NAND flash memory device that comprises a set of single level memory cells (SLCs) and a set of multi-level memory cells (MLCs), as shown in  FIGS. 3 and 4 , herein. In such embodiments, the processing unit  122  can write at least the portion of the data associated with the workload to the set of SLC memory cells or the set of MLC memory cells based, at least in part, on the characteristics of the workload. In some embodiments, the set of SLCs can be configured to store a look-up table to facilitate writing of at least the portion of the data to the other of the first memory device  123  or the second memory device  125 . 
     As used herein, the term “look-up table” generally refers to a data structure that contains indexing information that can correspond to desired output formats of data written to the memory system  104 . For example, the look-up table can include pre-fetched information that can be used by the memory system  104  to output various types of data processed by the memory system in a requested format. In some embodiments, the look-up table can be included in a flash memory device, such as the NAND memory device  333 , for example, in the SLC portion  335  of the NAND memory device  333 . The look-up table can store data corresponding to artificial intelligence and/or machine learning applications. In such embodiments, it may be beneficial to store the look-up table in a SLC portion of the memory device, as SLC memory generally offers high access speeds and accurate storage. In some embodiments, such artificial intelligence and/or machine learning applications can be executed in connection with performance of the operations described herein. 
     The embodiment of  FIG. 1  can include additional circuitry that is not illustrated so as not to obscure embodiments of the present disclosure. For example, the memory system  104  can include address circuitry to latch address signals provided over I/O connections through I/O circuitry. Address signals can be received and decoded by a row decoder and a column decoder to access the memory system  104  and/or the memory devices  123 ,  125 . It will be appreciated by those skilled in the art that the number of address input connections can depend on the density and architecture of the memory system  104  and/or the memory devices  123 ,  125 . 
       FIG. 2  is another functional block diagram in the form of a computing system  200  including an apparatus including a host  202  and a memory system  204  in accordance with a number of embodiments of the present disclosure. In some embodiments, the computing system  200  can be a mobile computing system (e.g., a mobile computing device  501 , such as a smartphone, a tablet, a phablet, and/or a IoT device, among others). The memory system  204  can include a number of different memory devices  223 ,  225 ,  227 , which can include one or more different media types  223 ,  225 ,  227 . The different memory devices  223 ,  225 , and/or  227  can include one or more memory modules (e.g., single in-line memory modules, dual in-line memory modules, etc.). The host  202 , memory system  204 , controller  220 , processing unit  222 , memory devices  223 ,  225  and/or the media types  224 ,  226  can be analogous to the host  102 , memory system  104 , controller  120 , processing unit  122 , memory devices  123 ,  125  and/or the media types  124 ,  126  illustrated in  FIG. 1 , herein. 
     In some embodiments, each of the memory devices  223 ,  225 , and  227  can be different types of memory devices. Accordingly, in some embodiments, each of the memory devices  223 ,  225 , and  227  can include different media types  224 ,  226 , and  228 . In a non-limiting example, the memory device  223  can be a volatile memory device, such as a DRAM device and can include a media type  224  that corresponds to a DRAM memory device (e.g., an array of memory cells that include at least one capacitor and at least one transistor). Continuing with this example, the memory device  225  can be a flash memory device, such as a NAND memory device and can include a media type  226  that corresponds to a NAND memory device (e.g., comprises an array of floating-gate metal-oxide-semiconductor field-effect transistors). In this non-limiting example, the memory device  227  can be an emerging memory device (e.g., the emerging memory device  439  illustrated in  FIG. 4 , herein), such as the emerging memory devices described above, and can include a media type  228  that corresponds to an emerging memory device (e.g., an array of resistance variable memory cells that are configured to perform bit storage based on a change in a bulk resistance associated with the resistance variable memory cells). 
     The memory devices  223 ,  225 , and  227  can be configured to read, write, and/or store data corresponding to one or more workloads executed by the computing system  200 . An application corresponding to the workload can be executed by, for example, the processing unit  222  to cause the data written to the memory devices  223 ,  225 , and  227  to be used in execution of the application and/o workload. As described above, the controller  220  can control writing at least a portion of the data to a different memory device than the memory device in which the data is initially written based on characteristics of the workload. 
     For example, if data corresponding to a particular workload is stored in the memory device  223 , the controller  220  and/or the processing unit  222  can, in response to a determination that the workload may be more efficiently executed (e.g., optimized) using a different memory device, cause at least a portion of the data corresponding to the particular workload to be written to the memory device  225  and/or to the memory device  227 . 
     In a non-limiting example, a system (e.g., the computing system  200  and/or the mobile computing device  501  illustrated in  FIG. 5 , herein) can include a memory system  204  comprising a processing unit  222 , a first memory device  223  comprising a first type of media  224 , a second memory device  225  comprising a second type of media  226 , and a third memory device  227  comprising a third type of media  228 . In some embodiments, the first memory device  223  can be a dynamic random-access memory device, the second memory device  225  can be a NAND flash memory device, and the third memory device  227  can be an emerging memory device, such as a 3D XP memory device, a self-selecting cell memory device, etc., as described above. 
     In at least one embodiment, the media type  224  comprises an array of memory cells that include at least one capacitor and at least one transistor, the media type  226  comprises an array of floating-gate metal-oxide-semiconductor field-effect transistors, and the type of media  228  comprises an array of resistance variable memory cells that are configured to perform bit storage based on a change in a bulk resistance associated with the resistance variable memory cells. 
     An imaging device (e.g., the imaging device  121  illustrated in  FIG. 1 , herein) can be coupled to the memory device  204 . In such examples, the processing unit  222  can receive signaling comprising information captured by the imaging device and execute, using data written to the memory device  223 , the memory device  225 , or the memory device  227 , a workload that includes processing the information detected by the imaging device. As discussed herein, the memory system  204  and the imaging device can be resident on a mobile computing device (e.g., the computing system  200  and/or the mobile computing device  501  illustrated in  FIG. 5 , herein). 
     The processing unit  222  can determine characteristics of the executed workload while the data is written to the memory device  223 , the memory device  225 , or the memory device  227  and write at least a portion of data associated with the workload to at least one of the other of the memory device  223 , the memory device  225 , or the memory device based  227 , at least on part, on the determined characteristics of the workload and the information detected by the imaging device. 
     In some embodiments, the processing unit  222  can write at least the portion of data associated with the workload to at the least one of the other of the memory device  223 , the memory device  225 , or the memory device  227  to optimize battery consumption of the mobile computing device as part of execution of the workload that includes processing the information detected by the imaging device. 
     As described herein, the memory system  204  and the imaging device can be resident on a mobile computing device and the processing unit  222  can receive data (e.g., images, streams of images, and/or live-streaming information) from the imaging device in conjunction with performance of a medical self-diagnostic test and the processing unit  222  can write at least a portion of the data from the imaging device to at least one of the other of the memory device  223 , the memory device  225 , or the memory device based  227 , at least on part, on a determined category associated with the medical self-diagnostic test. In this example, the processing unit  222  can execute, using at least the portion of the data captured by the imaging device written to the memory device  223 , the memory device  225 , or the memory device  227 , a workload that includes at least the portion of the data captured from the imaging device. 
     In such examples, the processing unit  222  can determine the characteristics of the executed workload while the data is written to the memory device  223 , the memory device  225 , or the memory device  227  by monitoring at least one of an access frequency of data associated with the workload, a latency associated with execution of the workload, and/or an amount of processing resources consumed in execution of the workload and write at least the portion of data associated with the workload to at least one of the other of the memory device  223 , the memory device  225 , or the memory device  227  based, at least on part, on the determined access frequency of data associated with the workload, the latency associated with execution of the workload, and/or the amount of processing resources consumed in execution of the workload. 
     In some embodiments, at least a portion of the data written to the memory device  223 , the memory device  225 , or the memory device  227  is formatted according to a universal number format or a posit format. In contrast to the IEEE 754 floating-point or fixed-point binary formats, which include a sign bit sub-set, a mantissa bit sub-set, and an exponent bit sub-set, universal number formats, such as posits include a sign bit sub-set, a regime bit sub-set, a mantissa bit sub-set, and an exponent bit sub-set. This can allow for the accuracy, precision, and/or the dynamic range of a posit to be greater than that of a float, or other numerical formats. In addition, posits can reduce or eliminate the overflow, underflow, NaN, and/or other corner cases that are associated with floats and other numerical formats. Further, the use of posits can allow for a numerical value (e.g., a number) to be represented using fewer bits in comparison to floats or other numerical formats. 
     As used herein, a “precision” refers to a quantity of bits in a bit string that are used for performing computations using the bit string. For example, if each bit in a 16-bit bit string is used in performing computations using the bit string, the bit string can be referred to as having a precision of 16 bits. However, if only 8-bits of a 16-bit bit string are used in performing computations using the bit string (e.g., if the leading 8 bits of the bit string are zeros), the bit string can be referred to as having a precision of 8-bits. As the precision of the bit string is increased, computations can be performed to a higher degree of accuracy. Conversely, as the precision of the bit string is decreased, computations can be performed using to a lower degree of accuracy. For example, an 8-bit bit string can correspond to a data range consisting of two hundred and fifty-five (256) precision steps, while a 16-bit bit string can correspond to a data range consisting of sixty-five thousand five hundred and thirty-six (63,536) precision steps. 
     As used herein, a “dynamic range” or “dynamic range of data” refers to a ratio between the largest and smallest values available for a bit string having a particular precision associated therewith. For example, the largest numerical value that can be represented by a bit string having a particular precision associated therewith can determine the dynamic range of the data format of the bit string. For a universal number (e.g., a posit) format bit string, the dynamic range can be determined by the numerical value of the exponent bit sub-set of the bit string. 
     A dynamic range and/or the precision can have a variable range threshold associated therewith. For example, the dynamic range of data can correspond to an application that uses the data and/or various computations that use the data. This may be due to the fact that the dynamic range desired for one application may be different than a dynamic range for a different application, and/or because some computations may require different dynamic ranges of data. Accordingly, embodiments herein can allow for the dynamic range of data to be altered to suit the requirements of disparate applications and/or computations. In contrast to approaches that do not allow for the dynamic range of the data to be manipulated to suit the requirements of different applications and/or computations, embodiments herein can improve resource usage and/or data precision by allowing for the dynamic range of the data to varied based on the application and/or computation for which the data will be used. 
       FIG. 3  is a functional block diagram in the form of an apparatus including a memory system  304  in accordance with a number of embodiments of the present disclosure.  FIG. 3  illustrates a memory system  304 , which can be analogous to the memory system  104  illustrated in  FIG. 1  and/or the memory system  204  illustrated in  FIG. 2 , herein. As shown in  FIG. 3 , the memory system  304  includes a controller  320  (which can be analogous to the controller  120  illustrated in  FIG. 1  and/or the controller  220  illustrated in  FIG. 2 , herein), a DRAM memory device  331  (which can be analogous to one of the memory devices  123 ,  125  illustrated in  FIG. 1  and/or one of the memory devices  223 ,  225 ,  227  illustrated in  FIG. 2 , herein), and a NAND memory device  333  (which can be analogous to one of the memory devices  123 ,  125  illustrated in  FIG. 1  and/or one of the memory devices  223 ,  225 ,  227  illustrated in  FIG. 2 , herein). 
     As shown in  FIG. 3 , the NAND memory device  333  can include various portions of memory cells, which can include a set of single level memory cells (SLCs)  335  and a set of multi-level memory cells (MLCs), such as a set of triple-level memory cells (TLCs)  337 . In some embodiments, the controller can cause at least a portion of data used by a workload executed on the memory system  304  to be written to the SLC portion  335  and/or or the TLC portion  337  based on the characteristics of the workload involving the data. 
     For example, data that is classified as hot data can be written to the SLC portion  335  while data that is classified as cold data can be written to the TLC portion  337 , or vice versa, as part of optimizing performance of the memory system  304  during execution of a workload. By selectively writing portions of data involved in the workload to different memory portions (e.g., to a SLC portion  335  and/or a TLC portion  337 ) of the NAND memory device  333 , performance of the computing system, especially during execution of workloads described herein, can be improved in comparison to some approaches. Embodiments are not so limited, however, and in some embodiments, hot data can be written to the DRAM memory device, colder data can be written to the NAND memory device  333 , and cold data can be written to the emerging memory device  339 . 
     For example, by selectively writing portions of data that correspond to workloads that benefit from rapid executed to the DRAM memory device  331  while writing portions of data that correspond to workloads that may not benefit as much from rapid execution to the SLC portion  335  and/or the TLC portion  337 , and/or to the emerging memory device  339 , workloads can be allocated to memory devices within the memory system  304  that can allow for optimized execution of the workloads within the memory system  304 . Rapidly. For similar reasons, portions of the workloads can be written to an emerging memory device (e.g., the emerging memory device  439  illustrated in  FIG. 4 , herein). 
     In some embodiments, at least a portion of the SLC portion  335  of the NAND memory device  333  can be allocated for storage of a look-up table. The look-up table can be a data structure that contains indexing information that can correspond to desired output formats of data written to or from the memory system  304 . For example, the look-up table can include pre-fetched information that can be used by the memory system  304  to output various types of data processed by the memory system in a requested format. In some embodiments, the look-up table can facilitate writing of at least a portion of data involved in a workload to one of the memory devices described herein. 
       FIG. 4  is another functional block diagram in the form of an apparatus including a memory system  404  in accordance with a number of embodiments of the present disclosure.  FIG. 4  illustrates a memory system  404 , which can be analogous to the memory system  104  illustrated in  FIG. 1 , the memory system  204  illustrated in  FIG. 2 , and/or the memory system  304  illustrated in  FIG. 3 , herein. 
     As shown in  FIG. 4 , the memory system  404  includes a controller  420  (which can be analogous to the controller  120  illustrated in  FIG. 1 , the controller  220  illustrated in  FIG. 2 , and/or the controller  320  illustrated in  FIG. 3 , herein), a DRAM memory device  431  (which can be analogous to one of the memory devices  123 ,  125  illustrated in  FIG. 1 , one of the memory devices  223 ,  225 ,  227  illustrated in  FIG. 2 , and/or one of the DRAM memory device  331  illustrated in  FIG. 3 , herein), a NAND memory device  433  (which can be analogous to one of the memory devices  123 ,  125  illustrated in  FIG. 1 , one of the memory devices  223 ,  225 ,  227  illustrated in  FIG. 2 , and/or the NAND memory device  333  illustrated in  FIG. 3 , herein), and an emerging memory device  439  (which can be analogous to one of the memory devices  123 ,  125  illustrated in  FIG. 1  and/or one of the memory devices  223 ,  225 ,  227  illustrated in  FIG. 2 , herein). 
     The DRAM memory device  431  can include an array of memory cells that include at least one transistor and one capacitor configured to store a charge corresponding to a single data bit. The NAND memory device  433  can include various portions of memory cells, which can include a set of single level memory cells (SLCs)  435  and a set of multi-level memory cells (MLCs), such as a set of triple-level memory cells (TLCs)  437 , which can be analogous to the SLC portion  335  and the TLC portion  337 , respectively, illustrated and described in connection with  FIG. 3 , herein. 
     The emerging memory device  439  can be an emerging memory device, as described above. For example, the emerging memory device  439  can be a resistance variable (e.g., 3-D Crosspoint (3D XP)) memory devices, memory devices that include an array of self-selecting memory (SSM) cells, etc., or any combination thereof. 
       FIG. 5  is a diagram illustrating a human medical self-diagnostic test subject  540  and a mobile computing device  501  in accordance with a number of embodiments of the present disclosure. As shown in  FIG. 5 , the mobile computing device  501  includes an imaging device  521 , which can be analogous to the imaging device  121  illustrated in  FIG. 1 , herein and a memory system  504 , which can be analogous to the memory system  104 ,  204 ,  304 ,  404  illustrated in  FIGS. 1-4 , herein. In some embodiments, the mobile computing device  501  can be analogous to the computing system  100  and/or the computing system  200  illustrated in  FIGS. 1 and 2 , respectively, herein. Embodiments are not so limited, however, and other areas of interest can include a nasal cavity, a stomach, a liver, a kidney, a lung, a brain, a muscle, a joint, a bone, and/or a ligament, among others. 
     The human medical self-diagnostic test subject  540  can include various areas of interest with respect to performance of medical self-diagnostic testing operations (e.g., the areas of interest  542 ,  544 ,  546 , and/or  548 ). The area of interest  542  can be an ear canal or other portion of an ear. The area of interest  544  can be an eyeball or other portion of an eye. The area of interest  546  can be a uterus or womb, and the area of interest  548  can be a genital area that can include one or more testicles. 
     As shown in  FIG. 5 , the imaging device  521  can receive information (e.g., images and/or video) related to one or more of the areas of interest  542 ,  544 ,  546 ,  548 . The information can be processed and/or analyzed within the mobile computing device  501  for example, using the memory system  504  resident on the mobile computing system  501 . In some embodiments, the information (e.g., the images and/or video) can be processed by the mobile computing device  501  as part of performance of a medical self-diagnostic test. 
     The information, which can include images and/or streaming (e.g., live-streaming) video can be processed by the mobile computing system  501  in connection with execution of one or more applications running on the mobile computing device  501 . As described above, execution of such applications can give rise to demanding workloads. Accordingly, as described herein, the information can be selectively written to different memory devices (e.g., the memory devices  223 ,  225 , and/or  227  illustrated in  FIG. 2 , herein), and therefore different media types (e.g., the media types  224 ,  226 , and/or  228  illustrated in  FIG. 2 , herein) based on characteristics of the workloads. 
     In some embodiments, the images and/or video can be processed and/or analyzed by the mobile computing device  501  during execution of an application to analyze the areas of interest  542 ,  544 ,  546 , and/or  548  illustrated in  FIG. 5 . In addition, the images and/or video can be processed and/or analyzed by the mobile computing device  501  to detect and/or replace one or more corrupted portions (e.g., pixels) of the images and/or video to recover and/or improve the quality of the images and/or video. 
       FIG. 6  is a flow diagram representing an example method corresponding to memory system workload allocation in accordance with a number of embodiments of the present disclosure. The method  650  can be performed by processing logic that can include hardware (e.g., processing unit(s), processing device(s), control circuitry, dedicated logic, programmable logic, microcode, hardware of a device, and/or integrated circuit(s), etc.), software (e.g., instructions run or executed on a processing unit), or a combination thereof. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in every embodiment. Other process flows are possible. 
     At block  652 , the method  650  can include receiving, by processing unit coupled to a first memory device comprising a first type of media and a second memory device comprising a second type of media, data captured from an imaging device coupled to the processing unit. In some embodiments, the characteristics of the workload can include a frequency of access of data associated with the workload during execution of the workload. The first memory device can be analogous to the memory device  123 ,  223 , while the second memory device can be analogous to the memory device  125 ,  225  illustrated in  FIGS. 1 and 2 , herein. Further, the first type of media can be analogous to the media type  124 ,  224 , while the second type of media can be analogous to the media type  126 ,  226  illustrated in  FIGS. 1 and 2 , herein. The imaging device The imaging device can be analogous to the imaging device  121 ,  521  illustrated in  FIGS. 1 and 5 , herein. 
     At block  654 , the method  650  can include determining, by the processing unit, characteristics of a workload corresponding to processing of the data that is written to the first memory device or the second memory device and characteristics of the data captured by the imaging device. 
     At block,  656 , the method  650  can include writing, by the processing unit, at least a portion of data associated with the workload to the other of the first memory device or the second memory device based, at least on part, on the determined characteristics of the workload and the data captured by the imaging device. 
     At block  658 , the method  650  can include causing, by the processing unit, the workload to be executed while at least the portion of the data associated with the workload and the data captured by the imaging device is written to the other of the first memory device or the second memory device. 
     In some embodiments, the method  650  can include determining, by the processing unit, that the data captured by the imaging device corresponds to performance of an operation to detect an abnormality in at least a portion of a human body. The operation to detect the abnormality in the body can be performed as part of a self-diagnostic medical test. In such embodiments, the method  650  can further include writing at least the portion of the data associated with the workload and the data captured by the imaging device to the other of the first memory device or the second memory device based, at least in part, on determining that the workload or the data captured by the imaging device, or both, corresponds to performance of the operation to detect the abnormality in at least the portion of the human body. 
     The method  650  can further include determining, by the processing unit, that the data captured by the imaging device corresponds to performance of an ultrasound imaging operation and writing at least the portion of the data associated with the workload and the data captured by the imaging device to the other of the first memory device or the second memory device based, at least in part, on determining that the workload or the data captured by the imaging device, or both corresponds to performance of the ultrasound imaging operation. 
     The method  650  can further include determining, by the processing unit, that the workload corresponds to performance of an operation to process an image or a video stream and writing at least the portion of the data associated with the workload to the other of the first memory device or the second memory device based, at least in part, on determining that the workload corresponds to performance of the operation to process the image or the video stream. In such embodiments, the method  650  can further include performing, by the processing unit, the operation to process the image or the video stream by exchanging at least one pixel of the image or the video stream, correcting a blurred portion of the image or the video stream, and/or removing noise from the image or the video stream. 
     As described above, the first memory device or the second memory device can be a non-persistent memory device, and the other of the first memory device or the second memory device can be a persistent memory device. In some embodiments, the processing unit, the first memory device, and the second memory device can be resident on a mobile computing device (e.g., the mobile computing device  501  illustrated in  FIG. 5 , herein). In such embodiments, the method  650  can include determining, writing, and causing, by the processing unit in the absence of control signals generated external to the mobile computing device. Embodiments are not so limited, and in some embodiments, the method  650  can include writing at least the portion of data associated with the workload to the other of the first memory device or the second memory device as part of an operation to optimize battery consumption of the mobile computing device. 
     Although specific embodiments have been illustrated and described herein, those of ordinary skill in the art will appreciate that an arrangement calculated to achieve the same results can be substituted for the specific embodiments shown. This disclosure is intended to cover adaptations or variations of one or more embodiments of the present disclosure. It is to be understood that the above description has been made in an illustrative fashion, and not a restrictive one. Combination of the above embodiments, and other embodiments not specifically described herein will be apparent to those of skill in the art upon reviewing the above description. The scope of the one or more embodiments of the present disclosure includes other applications in which the above structures and processes are used. Therefore, the scope of one or more embodiments of the present disclosure should be determined with reference to the appended claims, along with the full range of equivalents to which such claims are entitled. 
     In the foregoing Detailed Description, some features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the disclosed embodiments of the present disclosure have to use more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.