Patent Publication Number: US-2022222510-A1

Title: Multi-operational modes of neural engine circuit

Description:
BACKGROUND 
     1. Field of the Disclosure 
     The present disclosure relates a circuit for performing operations related to neural networks, and more specifically to a neural engine circuit that performs a convolution operation in one mode and a parallel sorting operation in another mode. 
     2. Description of the Related Arts 
     An artificial neural network (ANN) is a computing system or model that uses a collection of connected nodes to process input data. The ANN is typically organized into layers where different layers perform different types of transformation on their input. Extensions or variants of ANN such as convolution neural network (CNN), recurrent neural networks (RNN) and deep belief networks (DBN) have come to receive much attention. These computing systems or models often involve extensive computing operations including multiplication and accumulation. For example, CNN is a class of machine learning technique that primarily uses convolution between input data and kernel data, which can be decomposed into multiplication and accumulation operations. 
     Depending on the types of input data and operations to be performed, these machine learning systems or models can be configured differently. Such varying configuration would include, for example, pre-processing operations, the number of channels in input data, kernel data to be used, non-linear function to be applied to convolution result, and applying of various post-processing operations. Using a central processing unit (CPU) and its main memory to instantiate and execute machine learning systems or models of various configuration is relatively easy because such systems or models can be instantiated with mere updates to code. However, relying solely on the CPU for various operations of these machine learning systems or models would consume significant bandwidth of a central processing unit (CPU) as well as increase the overall power consumption. 
     SUMMARY 
     Embodiments relate to a neural engine circuit of a neural network processor circuit that performs a convolution operation on input data in a first mode and a parallel sorting operation on input data in a second mode. The neural engine circuit includes a plurality of operation circuits and an accumulator circuit coupled to the outputs of the plurality of operation circuits. The operation circuits receive input data. In the first mode, the plurality of operation circuits performs multiply-add operations of a convolution on input data using a kernel. In the second mode, the plurality of operation circuits performs a portion of a parallel sorting operation on input data. In the first mode, the accumulator circuit receives and stores first results of the multiply-add operations. In the second mode, the accumulator circuit receives and stores second results of the parallel sorting operation. 
     In one or more embodiments, the second results of the parallel sorting operation are sent to the plurality of operation circuits, in the second mode, to perform a subsequent portion of the parallel sorting operation. 
     In one or more embodiments, the parallel sorting operation implements a bitonic sorting network that simultaneously produces a maximum value and a minimum value of the input data. 
     In one or more embodiment, the accumulator circuit, in the second mode, receives and stores an index for a maximum value of the input data and an index for a minimum value of the input data. 
     In one or more embodiments, each of the operation circuits includes, a multiplier circuit, an adder circuit and a comparator circuit. The multiplier circuit receives and performs a multiplication operation on a portion of the input data and a portion of the kernel in the first mode. The adder circuit is coupled to the multiplier circuit and the accumulator circuit. The adder circuit receives and performs an adding operation on the stored first result and a value derived from the multiplication operation in the first mode. The comparator circuit is coupled to the accumulator circuit. The comparator circuit receives and performs a comparison operation on a portion of the input data and the stored second result in the second mode. 
     In one or more embodiments, the neural engine circuit further includes an input buffer circuit coupled to the operation circuits to provide different sets of the input data to the plurality of operation circuits in different cycles of the neural engine circuit. 
     In one or more embodiments, another neural engine circuit performs another portion of the parallel sorting operation in parallel with the neural engine circuit. 
     In one or more embodiments, the input data includes a plurality of input data elements, and the plurality of operation circuits is further configured to perform a portion of the parallel sorting operation on the plurality of input data elements in the second mode. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a high-level diagram of an electronic device, according to one embodiment. 
         FIG. 2  is a block diagram illustrating components in the electronic device, according to one embodiment. 
         FIG. 3  is a block diagram illustrating a neural processor circuit, according to one embodiment. 
         FIG. 4A  is a block diagram of neural engine, according to one embodiment. 
         FIG. 4B  is a block diagram of neural engine illustrating operations in a second mode, according to one embodiment. 
         FIG. 5  is a circuit diagram of operation circuit and accumulator circuit of a neural engine, according to one embodiment. 
         FIG. 6  is a flowchart illustrating a method of operating neural engine circuit in a first and second mode, according to one embodiment. 
         FIG. 7  is a conceptual diagram illustrating an example parallel sorting network performed at neural processor circuit, according to one embodiment. 
     
    
    
     The figures depict, and the detail description describes, various non-limiting embodiments for purposes of illustration only. 
     DETAILED DESCRIPTION 
     Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, the described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments. 
     Embodiments of the present disclosure relate to a neural engine circuit of a neural processor circuit that performs a convolution operation on input data in a first mode and a parallel sorting operation on input data in a second mode. The neural engine circuit includes a plurality of operation circuits that operates with an accumulator. During the first mode, the operation circuits perform multiply-add operations of a convolution on input data and kernel data. During the second mode, the operation circuits perform a portion of a parallel sorting operation on input data. 
     Exemplary Electronic Device 
     Embodiments of electronic devices, user interfaces for such devices, and associated processes for using such devices are described. In some embodiments, the device is a portable communications device, such as a mobile telephone, that also contains other functions, such as personal digital assistant (PDA) and/or music player functions. Exemplary embodiments of portable multifunction devices include, without limitation, the iPhone®, iPod Touch®, Apple Watch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other portable electronic devices, such as wearables, laptops or tablet computers, are optionally used. In some embodiments, the device is not a portable communication device, but is a desktop computer or other computing device that is not designed for portable use. In some embodiments, the disclosed electronic device may include a touch-sensitive surface (e.g., a touch screen display and/or a touchpad). An example electronic device described below in conjunction with Figure (FIG.)  1  (e.g., device  100 ) may include a touch-sensitive surface for receiving user input. The electronic device may also include one or more other physical user-interface devices, such as a physical keyboard, a mouse and/or a joystick. 
       FIG. 1  is a high-level diagram of an electronic device  100 , according to one embodiment. Device  100  may include one or more physical buttons, such as a “home” or menu button  104 . Menu button  104  is, for example, used to navigate to any application in a set of applications that are executed on device  100 . In some embodiments, menu button  104  includes a fingerprint sensor that identifies a fingerprint on menu button  104 . The fingerprint sensor may be used to determine whether a finger on menu button  104  has a fingerprint that matches a fingerprint stored for unlocking device  100 . Alternatively, in some embodiments, menu button  104  is implemented as a soft key in a graphical user interface (GUI) displayed on a touch screen. 
     In some embodiments, device  100  includes touch screen  150 , menu button  104 , push button  106  for powering the device on/off and locking the device, volume adjustment buttons  108 , Subscriber Identity Module (SIM) card slot  110 , headset jack  112 , and docking/charging external port  124 . Push button  106  may be used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device  100  also accepts verbal input for activation or deactivation of some functions through microphone  113 . Device  100  includes various components including, but not limited to, a memory (which may include one or more computer readable storage mediums), a memory controller, one or more central processing units (CPUs), a peripherals interface, an RF circuitry, an audio circuitry, speaker  111 , microphone  113 , input/output (I/O) subsystem, and other input or control devices. Device  100  may include one or more image sensors  164 , one or more proximity sensors  166 , and one or more accelerometers  168 . Device  100  may include more than one type of image sensors  164 . Each type may include more than one image sensor  164 . For example, one type of image sensors  164  may be cameras and another type of image sensors  164  may be infrared sensors for facial recognition that is performed by one or more machine learning models stored in device  100 . Device  100  may include components not shown in  FIG. 1  such as an ambient light sensor, a dot projector and a flood illuminator that is to support facial recognition. 
     Device  100  is only one example of an electronic device, and device  100  may have more or fewer components than listed above, some of which may be combined into a component or have a different configuration or arrangement. The various components of device  100  listed above are embodied in hardware, software, firmware or a combination thereof, including one or more signal processing and/or application-specific integrated circuits (ASICs). 
       FIG. 2  is a block diagram illustrating components in device  100 , according to one embodiment. Device  100  may perform various operations including implementing one or more machine learning models. For this and other purposes, device  100  may include, among other components, image sensors  202 , a system-on-a chip (SOC) component  204 , a system memory  230 , a persistent storage (e.g., flash memory)  228 , a motion sensor  234 , and a display  216 . The components as illustrated in  FIG. 2  are merely illustrative. For example, device  100  may include other components (such as speaker or microphone) that are not illustrated in  FIG. 2 . Further, some components (such as motion sensor  234 ) may be omitted from device  100 . 
     An image sensor  202  is a component for capturing image data and may be embodied, for example, as a complementary metal-oxide-semiconductor (CMOS) active-pixel sensor a camera, video camera, or other devices. Image sensor  202  generates raw image data that is sent to SOC component  204  for further processing. In some embodiments, the image data processed by SOC component  204  is displayed on display  216 , stored in system memory  230 , persistent storage  228  or sent to a remote computing device via network connection. The raw image data generated by image sensor  202  may be in a Bayer color kernel array (CFA) pattern. 
     Motion sensor  234  is a component or a set of components for sensing motion of device  100 . Motion sensor  234  may generate sensor signals indicative of orientation and/or acceleration of device  100 . The sensor signals are sent to SOC component  204  for various operations such as turning on device  100  or rotating images displayed on display  216 . 
     Display  216  is a component for displaying images as generated by SOC component  204 . Display  216  may include, for example, liquid crystal display (LCD) device or an organic light-emitting diode (OLED) device. Based on data received from SOC component  204 , display  116  may display various images, such as menus, selected operating parameters, images captured by image sensor  202  and processed by SOC component  204 , and/or other information received from a user interface of device  100  (not shown). 
     System memory  230  is a component for storing instructions for execution by SOC component  204  and for storing data processed by SOC component  204 . System memory  230  may be embodied as any type of memory including, for example, dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate (DDR, DDR2, DDR3, etc.) RAMBUS DRAM (RDRAM), static RAM (SRAM) or a combination thereof. 
     Persistent storage  228  is a component for storing data in a non-volatile manner. Persistent storage  228  retains data even when power is not available. Persistent storage  228  may be embodied as read-only memory (ROM), flash memory or other non-volatile random access memory devices. Persistent storage  228  stores an operating system of device  100  and various software applications. Persistent storage  228  may also store one or more machine learning models, such as regression models, random forest models, support vector machines (SVMs) such as kernel SVMs, and artificial neural networks (ANNs) such as convolutional network networks (CNNs), recurrent network networks (RNNs), autoencoders, and long short term memory (LSTM). A machine learning model may be an independent model that works with the neural processor circuit  218  and various software applications or sensors of device  100 . A machine learning model may also be part of a software application. The machine learning models may perform various tasks such as facial recognition, image classification, object, concept, and information classification, speech recognition, machine translation, voice recognition, voice command recognition, text recognition, text and context analysis, other natural language processing, predictions, and recommendations. 
     Various machine learning models stored in device  100  may be fully trained, untrained, or partially trained to allow device  100  to reinforce or continue to train the machine learning models as device  100  is used. Operations of the machine learning models include various computation used in training the models and determining results in runtime using the models. For example, in one case, device  100  captures facial images of the user and uses the images to continue to improve a machine learning model that is used to lock or unlock the device  100 . 
     SOC component  204  is embodied as one or more integrated circuit (IC) chips and performs various data processing processes. SOC component  204  may include, among other subcomponents, image signal processor (ISP)  206 , a central processor unit (CPU)  208 , a network interface  210 , sensor interface  212 , display controller  214 , neural processor circuit  218 , graphics processor unit (GPU)  220 , memory controller  222 , video encoder  224 , storage controller  226 , and bus  232  connecting these subcomponents. SOC component  204  may include more or fewer subcomponents than those shown in  FIG. 2 . 
     ISP  206  is a circuit that performs various stages of an image processing pipeline. In some embodiments, ISP  206  may receive raw image data from image sensor  202 , and process the raw image data into a form that is usable by other subcomponents of SOC component  204  or components of device  100 . ISP  206  may perform various image-manipulation operations such as image translation operations, horizontal and vertical scaling, color space conversion and/or image stabilization transformations. 
     CPU  208  may be embodied using any suitable instruction set architecture, and may be configured to execute instructions defined in that instruction set architecture. CPU  208  may be general-purpose or embedded processors using any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, RISC, ARM or MIPS ISAs, or any other suitable ISA. Although a single CPU is illustrated in  FIG. 2 , SOC component  204  may include multiple CPUs. In multiprocessor systems, each of the CPUs may commonly, but not necessarily, implement the same ISA. 
     Graphics processing unit (GPU)  220  is graphics processing circuitry for performing graphical data. For example, GPU  220  may render objects to be displayed into a frame buffer (e.g., one that includes pixel data for an entire frame). GPU  220  may include one or more graphics processors that may execute graphics software to perform a part or all of the graphics operation, or hardware acceleration of certain graphics operations. 
     Neural processor circuit  218  is a circuit that performs various machine learning operations based on computation including multiplication, addition, comparison, and accumulation. Such computation may be arranged to perform, for example, various types of tensor multiplications such as tensor product and convolution of input data and kernel data. Neural processor circuit  218  is a configurable circuit that performs these operations in a fast and power-efficient manner while relieving CPU  208  of resource-intensive operations associated with neural network operations. Neural processor circuit  218  may receive the input data from sensor interface  212 , the image signal processor  206 , persistent storage  228 , system memory  230  or other sources such as network interface  210  or GPU  220 . The output of neural processor circuit  218  may be provided to various components of device  100  such as image signal processor  206 , system memory  230  or CPU  208  for various operations. The structure and operation of neural processor circuit  218  are described below in detail with reference to  FIG. 3 . 
     Network interface  210  is a subcomponent that enables data to be exchanged between devices  100  and other devices via one or more networks (e.g., carrier or agent devices). For example, video or other image data may be received from other devices via network interface  210  and be stored in system memory  230  for subsequent processing (e.g., via a back-end interface to image signal processor  206 ) and display. The networks may include, but are not limited to, Local Area Networks (LANs) (e.g., an Ethernet or corporate network) and Wide Area Networks (WANs). The image data received via network interface  210  may undergo image processing processes by ISP  206 . 
     Sensor interface  212  is circuitry for interfacing with motion sensor  234 . Sensor interface  212  receives sensor information from motion sensor  234  and processes the sensor information to determine the orientation or movement of device  100 . 
     Display controller  214  is circuitry for sending image data to be displayed on display  216 . Display controller  214  receives the image data from ISP  206 , CPU  208 , graphic processor or system memory  230  and processes the image data into a format suitable for display on display  216 . 
     Memory controller  222  is circuitry for communicating with system memory  230 . Memory controller  222  may read data from system memory  230  for processing by ISP  206 , CPU  208 , GPU  220  or other subcomponents of SOC component  204 . Memory controller  222  may also write data to system memory  230  received from various subcomponents of SOC component  204 . 
     Video encoder  224  is hardware, software, firmware or a combination thereof for encoding video data into a format suitable for storing in persistent storage  228  or for passing the data to network interface  210  for transmission over a network to another device. 
     In some embodiments, one or more subcomponents of SOC component  204  or some functionality of these subcomponents may be performed by software components executed on neural processor circuit  218 , ISP  206 , CPU  208  or GPU  220 . Such software components may be stored in system memory  230 , persistent storage  228  or another device communicating with device  100  via network interface  210 . 
     Example Neural Processor Circuit 
     Neural processor circuit  218  is a programmable circuit that performs machine learning operations on the input data of neural processor circuit  218 . Machine learning operations may include different computations for training of a machine learning model and for performing inference or prediction based on the trained machine learning model. 
     Taking an example of a CNN as the machine learning model, training of the CNN may include forward propagation and backpropagation. A neural network may include an input layer, an output layer, and one or more intermediate layers that may be referred to as hidden layers. Each layer may include one or more nodes, which may be fully or partially connected to other nodes in adjacent layers. In forward propagation, the neural network performs computation in the forward direction based on outputs of a preceding layer. The operation of a node may be defined by one or more functions. The functions that define the operation of a node may include various computation operation such as convolution of data with one or more kernels, parallel sorting of data, pooling of layers, tensor multiplication, etc. The functions may also include an activation function that adjusts the weight of the output of the node. Nodes in different layers may be associated with different functions. For example, a CNN may include one or more convolutional layers that are mixed with pooling layers and are followed by one or more fully connected layers. 
     Each of the functions, including kernels, in a machine learning model may be associated with different coefficients that are adjustable during training. In addition, some of the nodes in a neural network each may also be associated with an activation function that decides the weight of the output of the node in a forward propagation. Common activation functions may include step functions, linear functions, sigmoid functions, hyperbolic tangent functions (tanh), and rectified linear unit functions (ReLU). After a batch of data of training samples passes through a neural network in the forward propagation, the results may be compared to the training labels of the training samples to compute the network&#39;s loss function, which represents the performance of the network. In turn, the neural network performs backpropagation by using coordinate descent such as stochastic coordinate descent (SGD) to adjust the coefficients in various functions to improve the value of the loss function. 
     In training, device  100  may use neural processor circuit  218  to perform all or some of the operations in the forward propagation and backpropagation. Multiple rounds of forward propagation and backpropagation may be performed by neural processor circuit  218 , solely or in coordination with other processors such as CPU  208 , GPU  220 , and ISP  206 . Training may be completed when the loss function no longer improves (e.g., the machine learning model has converged) or after a predetermined number of rounds for a particular set of training samples. As device  100  is used, device  100  may continue to collect additional training samples for the neural network. 
     For prediction or inference, device  100  may receive one or more input samples. Neural processor circuit  218  may take the input samples to perform forward propagation to determine one or more results. The input samples may be images, speeches, text files, sensor data, or other data. 
     Data and functions (e.g., input data, kernels, functions, layers outputs, gradient data) in machine learning may be saved and represented by one or more tensors. Common operations related to training and runtime of a machine learning model may include tensor product, tensor transpose, tensor elementwise operation, convolution, sorting, application of an activation function, automatic differentiation to determine gradient, statistics and aggregation of values in tensors (e.g., average, variance, standard deviation), tensor rank and size manipulation, etc. 
     While the training and runtime of a neural network is discussed as an example, the neural processor circuit  218  may also be used for the operations of other types of machine learning models, such as a kernel SVM. 
     Referring to  FIG. 3 , an example neural processor circuit  218  may include, among other components, neural task manager  310 , a plurality of neural engines  314 A through  314 N (hereinafter collectively referred as “neural engines  314 ” and individually also referred to as “neural engine  314 ”), kernel direct memory access (DMA)  324 , data processor circuit  318 , data processor DMA  320 , and planar engine  340 . Neural processor circuit  218  may include fewer or additional components not illustrated in  FIG. 3 . 
     Each of neural engines  314  performs computing operations for machine learning in parallel. Depending on the load of operation, the entire set of neural engines  314  may be operating or only a subset of the neural engines  314  may be operating while the remaining neural engines  314  are placed in a power-saving mode to conserve power. Each of neural engines  314  includes components for storing one or more kernels, for performing multiply-accumulate operations, for performing parallel sorting operations, and for post-processing to generate an output data  328 , as described below in detail with reference to  FIGS. 4A and 4B . Neural engines  314  may specialize in performing computation heavy operations such as convolution operations and tensor product operations. Convolution operations may include different kinds of convolutions, such as cross-channel convolutions (a convolution that accumulates values from different channels), channel-wise convolutions, and transposed convolutions. 
     Planar engine  340  may specialize in performing simpler computing operations whose speed may primarily depend on the input and output (I/O) speed of the data transmission instead of the computation speed within planar engine  340 . Those computing operations may be referred to as I/O bound computations. In contrast, neural engines  314  may focus on complex computation whose speed may primarily depend on the computation speed within each neural engine  314 . For example, planar engine  340  is efficient at performing operations within a single channel while neural engines  314  are efficient at performing operations across multiple channels that may involve heavy accumulation of data. The use of neural engine  314  to compute I/O bound computations may not be efficient in terms of both speed and power consumption. In one embodiment, input data may be a tensor whose rank is larger than three (e.g., having three or more dimensions). A set of dimensions (two or more) in the tensor may be referred to as a plane while another dimension may be referred to as a channel. Neural engines  314  may convolve data of a plane in the tensor with a kernel and accumulate results of the convolution of different planes across different channels. On the other hand, planar engine  340  may specialize in operations within the plane. 
     The circuitry of planar engine  340  may be programmed for operation in one of multiple modes, including a pooling mode, an elementwise mode, and a reduction mode. In the pooling mode, planar engine  340  reduce a spatial size of input data. In the elementwise mode, planar engine  340  generates an output that is derived from elementwise operations of one or more inputs. In the reduction mode, planar engine  340  reduces the rank of a tensor. For example, a rank  5  tensor may be reduced to a rank  2  tensor, or a rank  3  tensor may be reduced to a rank 0 tensor (e.g., a scalar). 
     Neural task manager  310  manages the overall operation of neural processor circuit  218 . Neural task manager  310  may receive a task list from a compiler executed by CPU  208 , store tasks in its task queues, choose a task to perform, and send task commands to other components of the neural processor circuit  218  for performing the chosen task. Data may be associated with a task command that indicates the types of operations to be performed on the data. Data of the neural processor circuit  218  includes input data that is transmitted from another source such as system memory  230 , and data generated by the neural processor circuit  218  in a previous operation cycle. Each dataset may be associated with a task command that specifies the type of operations to be performed on the data. Neural task manager  310  may also perform switching of tasks on detection of events such as receiving instructions from CPU  208 . In one or more embodiments, neural task manager  310  sends rasterizer information to the components of neural processor circuit  218  to enable each of the components to track, retrieve or process appropriate segments of the input data and kernel data. For example, neural task manager  310  may include registers that stores the information regarding the size and rank of a dataset for processing by the neural processor circuit  218 . Although neural task manager  310  is illustrated in  FIG. 3  as part of neural processor circuit  218 , neural task manager  310  may be a component outside the neural processor circuit  218 . 
     Kernel DMA  324  is a read circuit that fetches kernel data from a source (e.g., system memory  230 ) and sends kernel data  326 A through  326 N to each of the neural engines  314 . Kernel data represents information from which kernel elements can be extracted. In one embodiment, the kernel data may be in a compressed format which is decompressed at each of neural engines  314 . Although kernel data provided to each of neural engines  314  may be the same in some instances, the kernel data provided to each of neural engines  314  is different in most instances. In one embodiment, the direct memory access nature of kernel DMA  324  may allow kernel DMA  324  to fetch and write data directly from the source without the involvement of CPU  208 . 
     Data processor circuit  318  manages data traffic and task performance of neural processor circuit  218 . Data processor circuit  318  may include a flow control circuit  332  and a buffer  334 . Buffer  334  is temporary storage for storing data associated with operations of neural processor circuit  218  and planar engine  340 , such as input data that is transmitted from system memory  230  (e.g., data from a machine learning model) and other data that is generated within neural processor circuit  218  or planar engine  340 . The data stored in data processor circuit  318  may include different subsets that are sent to various downstream components, such as neural engines  314  and planar engine  340 . 
     In one embodiment, buffer  334  is embodied as a non-transitory memory that can be accessed by neural engines  314  and planar engine  340 . Buffer  334  may store input data  322 A through  322 N for feeding to corresponding neural engines  314 A through  314 N or planar engine  340 , as well as output data  328 A through  328 N from each of neural engines  314 A through  314 N or planar engine  340  for feeding back into one or more neural engines  314  or planar engine  340 , or sending to a target circuit (e.g., system memory  230 ). Buffer  334  may also store input data  342  and output data  344  of planar engine  340  and allow the exchange of data between neural engine  314  and planar engine  340 . For example, one or more output data  328 A through  328 N of neural engines  314  are used as the input  342  to planar engine  340 . Likewise, the output  344  of planar engine  340  may be used as the input data  322 A through  322 N of neural engines  314 . The inputs of neural engines  314  or planar engine  340  may be any data stored in buffer  334 . For example, in various operating cycles, the source datasets from which one of the engines fetches as inputs may be different. The input of an engine may be an output of the same engine in previous cycles, outputs of different engines, or any other suitable source datasets stored in buffer  334 . Also, a dataset in buffer  334  may be divided and sent to different engines for different operations in the next operating cycle. Two datasets in buffer  334  may also be joined for the next operation. 
     Flow control circuit  332  of data processor circuit  318  may control the exchange of data between neural engines  314  and planar engine  340 . The operations of data processor circuit  318  and other components of neural processor circuit  218  are coordinated so that the input data and intermediate data stored in data processor circuit  318  may be reused across multiple operations at neural engines  314  and planar engine  340 , thereby reducing data transfer to and from system memory  230 . Flow control circuit  332  may perform one or more of the following operations: (i) monitor the size and rank of data (e.g. data may be one or more tensors) that are being processed by neural engines  314  and planar engine  340 , (ii) determine which subsets of data are transmitted to neural engines  314  or to planar engine  340  based on the task commands associated with different subsets of data, (iii) determine the manner in which data is transmitted to neural engines  314  and planar engine  340  (e.g., the data processor circuit  318  may operate in a broadcast mode where the same data is fed to multiple input channels of neural engines  314  so that multiple or all neural engines  314  receive the same data or in a unicast mode where different neural engines  314  receives different data), and (iv) transmit a configuration command to the planar engine  340  to direct planar engine  340  to program itself for operating in one of multiple operation modes. 
     The data of neural processor circuit  218  stored in buffer  334  may be part of, among others, image data, histogram of oriented gradients (HOG) data, audio data, metadata, output data  328  of a previous cycle of a neural engine  314 , and other processed data received from other components of the SOC component  204 . 
     Data processor DMA  320  includes a read circuit that receives a segment of the input data from a source (e.g., system memory  230 ) for storing in buffer  334 , and a write circuit that forwards data from buffer  334  to a target component (e.g., system memory). In one embodiment, the direct memory access nature of data processor DMA  320  may allow data processor DMA  320  to fetch and write data directly from a source (e.g., system memory  230 ) without the involvement of CPU  208 . Buffer  334  may be a direct memory access buffer that stores data of a machine learning model of device  100  without involvement of CPU  208 . 
     Example Neural Engine Architecture 
       FIG. 4A  is a block diagram of neural engine  314 , according to one embodiment. Specifically,  FIG. 4A  illustrates neural engine  314  perform operations including operations to facilitate machine learning such as convolution, tensor product, and other operations that may involve heavy computation in the first mode. For this purpose, neural engine  314  receives input data  322 , performs multiply-accumulate operations (e.g., convolution operations) on input data  322  based on stored kernel data, performs further post-processing operations on the result of the multiply-accumulate operations, and generates output data  328 . Input data  322  and/or output data  328  of neural engine  314  may be of a single channel or span across multiple channels. 
     Neural engine  314  may include, among other components, input buffer circuit  402 , computation core  416 , neural engine (NE) control  418 , kernel extract circuit  432 , accumulator  414 , and output circuit  424 . Neural engine  314  may include fewer components than what is illustrated in  FIG. 4A  or include further components not illustrated in  FIG. 4A . 
     In the first mode, input buffer circuit  402  stores a subset of the data of neural processor circuit  218  as the subset of data is received from a source. The source may be data processor circuit  318 , planar engine  340 , or another suitable component. Input buffer circuit  402  sends an appropriate segment  408  of data for a current task or process loop to computation core  416  for processing. Input buffer circuit  402  may include a shifter  410  that shifts read locations of input buffer circuit  402  to change segment  408  of data sent to computation core  416 . By changing segments of input data  322  provided to computation core  416  via shifting, neural engine  314  can perform multiply-accumulate for different segments of input data  322  based on a fewer number of read operations. In one or more embodiments, the data of neural processor circuit  218  includes data of different convolution groups and/or input channels. 
     In the first mode, kernel extract circuit  432  receives kernel data  326  from kernel DMA  324  and extracts kernel coefficients  422 . In one embodiment, kernel extract circuit  432  references a lookup table (LUT) and uses a mask to reconstruct a kernel from compressed kernel data  326  based on the LUT. The mask indicates locations in the reconstructed kernel to be padded with zero and remaining locations to be filled with numbers. Kernel coefficients  422  of the reconstructed kernel are sent to computation core  416  to populate registers in operation circuits of computation core  416 . In other embodiments, kernel extract circuit  432  receives kernel data  326  in an uncompressed format and the kernel coefficients  422  are determined without referencing a LUT or using a mask. 
     In the first mode, computation core  416  performs computation operations. For this purpose, computation core  416  may include operation circuits OC 0  through OCN and a post-processor  428 . Each of operation circuits OC 0  through OCN may store an input value in the segment  408  of the input data and a corresponding kernel coefficient in kernel coefficients  422 . The input value and the corresponding kernel coefficient are multiplied in each of operation circuits OC 0  through OCN to generate a processed value  412 . 
     In the first mode, accumulator  414  receives and stores processed values  412  as a first result from operation circuits. The processed values stored in accumulator  414  may be sent back as feedback information  419  for further multiply and add operations at operation circuits or sent to post-processor  428  for post-processing. In the first mode, accumulator  414  in combination with operation circuits form a multiply-accumulator (MAC)  404 . In one or more embodiments, accumulator  414  may have subunits where each subunit sends data to different components of neural engine  314 . For example, during a processing cycle, data stored in a first subunit of accumulator  414  is sent to the MAC  404  while data stored in a second subunit of accumulator  414  is sent to post-processor  428 . 
     In the first mode, post-processor  428  performs further processing of values  412  received from accumulator  414 . Post-processor  428  may perform operations including, but not limited to, applying linear functions (e.g., Rectified Linear Unit (ReLU)), normalized cross-correlation (NCC), merging the results of performing neural operations on 8-bit data into 16-bit data, and local response normalization (LRN). The result of such operations is output from post-processor  428  as processed values  417  to output circuit  424 . In some embodiments, the processing at the post-processor  428  is bypassed. For example, the data in accumulator  414  may be sent directly to output circuit  424  for access by other components of neural processor circuit  218 . 
     NE control  418  controls operations of other components of neural engine  314  based on the operation modes and parameters of neural processor circuit  218 . Depending on different modes of operation (e.g., first mode or second mode), different convolution modes of operation (e.g., group convolution mode or non-group convolution mode), or parameters (e.g., the number of input channels and the number of output channels), neural engine  314  may operate on different input data in different sequences, return different values from accumulator  414  to operation circuits, and perform different types of post-processing operations at post-processor  428 . To configure components of neural engine  314  to operate in a desired manner, the NE control  418  sends task commands that may be included in information  419  to components of neural engine  314 . NE control  418  may include a rasterizer  430  that tracks the current task or process loop being processed at neural engine  314 . 
     In the first mode, rasterizer  430  may perform the operations associated with dividing the input data into smaller units (segments) and regulating the processing of the smaller units through the operation circuits and accumulator  414 . Rasterizer  430  keeps track of sizes and ranks of segments of the input/output data (e.g., groups, work units, input channels, output channels) and instructs the components of a neural processor circuit  218  for proper handling of the segments of the input data. For example, rasterizer  430  operates shifters  410  in input buffer circuits  402  to forward correct segments  408  of input data  322  to MAC  404  and send the finished output data  328  to data buffer  334 . Other components of neural processor circuit  218  (e.g., kernel DMA  324 , buffer DMA  320 , data buffer  334 , planar engine  340 ) may also have their corresponding rasterizers to monitor the division of input data and the parallel computation of various segments of input data  322  in different components. 
     In the first mode, output circuit  424  receives processed values  417  from post-processor  428  and interfaces with data processor circuit  318  to store processed values  417  in data processor circuit  318 . For this purpose, output circuit  424  may send out as output data  328  in a sequence or a format that is different from the sequence or format in which the processed values  417  are processed in post-processor  428 . 
     The components in neural engine  314  may be configured during a configuration period by NE control  418  and neural task manager  310 . For this purpose, neural task manager  310  sends configuration information to neural engine  314  during the configuration period. The configurable parameters and modes may include, but are not limited to, mapping between input data elements and kernel elements, the number of input channels, the number of output channels, performing of output strides, and enabling/selection of post-processing operations at post-processor  428 . 
       FIG. 4B  is a block diagram of neural engine  314  illustrating operations in a second mode, according to one embodiment. In the second mode, neural engine  314  performs parallel sorting operations (e.g., bitonic sorting, top-k bitonic sorting, counting sort, radix sort, Batcher odd-even merge sort, pairwise sort, etc.). For this purpose, neural engine  314  receives input data  322 , performs a portion of a parallel sorting operation (e.g., a comparison operation) on input data  322 , performs further post-processing operations on the result of the parallel sorting operation, and generates output data  328 . 
     In the second mode, input buffer circuit  402  stores a subset of the data of neural processor circuit  218  as the subset of data is received from a source (e.g., data processor circuit  318 , planar engine  340 , or another suitable component). Input buffer circuit  402  sends an appropriate segment  408  of data for a current task or process loop to computation core  416  for processing. For example, segment  408  of data may include a single vector or a plurality of vectors. Input buffer circuit  402  may include a shifter  410  that shifts read locations of input buffer circuit  402  to change segment  408  of data sent to computation core  416 . For example, at a first time, input buffer circuit  402  may send a first vector to computation core  416  and, at a second time, input buffer circuit  402  may send a second vector to computation core  416 . By changing segments of input data  322  provided to computation core  416  via shifting, neural engine  314  can perform various portions of a parallel sorting operation for different segments of input data  322  based on a fewer number of read operations to data processor circuit  318  or system memory  230 . 
     In the second mode, kernel extract circuit  432  is disabled by the NE control  418  while operation circuits OC 0  through OCN perform sorting operations on the segment  408  of the input data. Specifically, each of operations circuits OC 0  through OCN compares an input value to a previous input value to generate a processed value  412 . In some embodiments, processed value  412  may be a maximum value or a minimum value. In some embodiments, processed value  412  may include a maximum value and metadata corresponding to the maximum value (e.g., an index indicating value is greater) or a minimum value and metadata corresponding to the minimum value (e.g., an index indicating value is lesser). 
     In the second mode, accumulator  414  receives and stores a result of comparison operation as the second result from operation circuits. The processed values  412  stored in accumulator  414  may be sent back as feedback information  419  for further comparison operations (e.g., for performing a subsequent portion of the parallel sorting operation) at operation circuits OC 0  through OCN or sent to post-processor  428  for post-processing. In the second mode, accumulator  414  in combination with operation circuits OC 0  through OCN form a comparator-accumulator (CMP-AC)  434 . In one or more embodiments, accumulator  414  may have subunits where each subunit sends data to different components of neural engine  314 . For example, during a processing cycle, data stored in a first subunit of accumulator  414  is sent to CMP-AC  434  while data stored in a second subunit of accumulator  414  is sent to post-processor  428 . 
     In the second mode, post-processor  428 , NE control  418  and output circuit  424  may perform substantially the same operations as in the first mode described in  FIG. 4A . Rasterizer  430 , in the second mode, also performs operations similar to what is performed during the first mode except that it manages and tracks dividing of the input data into smaller units (segments) for sorting operations through CMP-AC  434  and accumulator  414 . 
     Example Operation Circuit and Accumulator Circuit Diagram 
       FIG. 5  is a circuit diagram of operation circuit OC 0  and accumulator circuit  414  of a neural engine  314 , according to one embodiment. Operation circuit OC 0  and accumulator circuit  414  may be programmed to perform in either the first mode or the second mode. In the first mode, operation circuit OC 0  may perform multiply-add operations of a convolution on a segment  408  of data and accumulator circuit  414  may operate to receive and store first results  524  (e.g., processed values  412 ) of the multiply-add operations. Although only a single operation circuit OC 0  is described in  FIG. 5 , other operation circuits OC 1  through OCN may have the same structure and operate in the same manner as operation circuit OC 0 . 
     In the second mode, operation circuit OC 0  may perform at least a portion of a parallel sorting operation to sort the segment  408  of data according to the size. For these purposes, operation circuit OC 0  may include, among other components, a multiplexor  530 , a multiplier  540 , an adder  542 , and a comparator circuit  550 . Operation circuit OC 0  may include fewer components or further components not illustrated in  FIG. 5 . For example, operation circuit OC 0  may not include comparator circuit  550  and adder  542  may be utilized as a subtractor in the second mode. Each component in operation circuit OC 0  may be embodied as a circuit or a circuit in combination with firmware or software. 
     During operation, operation circuit OC 0  receives a segment  408  of data as input at multiplexor  530 . Select line  510  controls output of the multiplexor  530 . In the first mode, a control signal sent over select line  510  may instruct multiplexor  530  to output segment  408  of data to multiplier  540 . In the second mode, the control signal sent over select line  510  may instruct multiplexor  530  to output segment  408  of data to comparator circuit  550 . In some embodiments, the control signal is set by neural task manager  310  and stored in a register of neural engine  314 . 
     In the first mode, multiplier  540  multiplies segment  408  of data with a corresponding kernel coefficient of kernel coefficients  422  that results in product value  520  corresponding to a multiplied value of the kernel coefficient and the data. Multiplier  540  provides product value  520  to adder  542 . Accumulator  414  provides stored first result  522  (generated as a result of prior adding operation at adder  542 ) to adder  542  so that adder  542  adds product value  520  to the stored first result  522 . After accumulated value  524  is generated by adder  542 , accumulated value  524  is again sent to accumulator  414  as first result  522 . Updated first result  522  can then be accumulated with another product value  520  in a next round. The process is repeated until all segment  408  of data and corresponding kernel coefficients  422  are processed. When accumulator  414  does not have a stored first result  522  to provide to adder  542  (e.g., in the first round of processing), adder  542  passes product value  520  to accumulator  414  so that the first product value  520  can be stored as an initial first result  522 . 
     In the second mode, comparator circuit  550  receives segment  408  of data from multiplexor  530 . When a first data value of segment  408  of data is received, it passes comparator circuit  550  and is stored in accumulator  414 . The first data value may be tagged with an initial index for the sorting operation. In the next round, a second data value is received as segment  408  of data and is fed to comparator circuit  550 . Furthermore, the first data value  526  is retrieved from accumulator  414  and fed to comparator circuit  550 . Comparator circuit  550  compares the first data value  526  and the second data value, updates their tagged indices to indicate which one of the first or second data value is larger, and forwards the second data value with its tagged index as a second result  528  to accumulator  414  for storing. 
     Subsequently, one of the stored vector/scalar values  526  is fed to comparator circuit  550  and compared with a new data value received as the next segment  408  of data. The process of feeding one of the stored data values  526  to the comparator circuit  550  and comparing it with a new data value is repeated until comparison operations are completed. The sequence of data values to be compared may be set by rasterizer  430  and may implement a parallel sorting network, as described below in detail with reference to  FIG. 7 . As each round of comparison is performed, the indices tagged to each vector/scalar value is updated to reflect the order to be sorted. 
     In alternative embodiments, the data values are not tagged with indices. For example, the comparator circuit  550  compares the first data value  526  and the second data value and forwards the first data value  526  and the second data value in either an increasing or decreasing order as the second result  528  to accumulator  414  for storing. 
     In embodiments of operation circuit OC 0  implemented with no comparator circuit  550  and adder  542  is utilized as a subtractor, the first data value  526  is retrieved from accumulator  414  and fed to adder  542 . Adder  542  subtracts the first data value  526  and the second data value. A sign-bit of the result determines which of the first data value  526  or second data value is forwarded as a second result  528  to accumulator  414  for storing. Subsequently, one of the stored vector/scalar values  526  is fed to adder  542  and subtracted with a new data value received as the next segment  408  of data. 
     While operation circuit OC 0  is performing its comparison operations in the second mode, one or more of the other operation circuit OC 1  through OCN may perform their own comparisons in parallel. 
     Example Process of Operating Neural Engine Circuit 
       FIG. 6  is a flowchart illustrating a method of operating neural engine circuit  314  in a first and second mode, according to one embodiment. Neural processor circuit  218  (e.g., via neural task manager  310 ) sets  610  a mode of neural engine circuit  314 . 
     When set to the first mode, the operation circuits (e.g., OC 0  through OCN of MAC  404 ) of neural engine circuit  314  receive  620  input data (e.g., each operation circuit receives a segment  408  of data). The operation circuits perform  630  multiply-add operations of a convolution on the input data using a kernel (e.g., a kernel coefficient of the kernel coefficients  422 ). Accumulator  414  receives and stores  640  accumulated value as a first results of the multiply-add operations. 
     When set to the second mode, the plurality of operation circuits (e.g., OC 0  through OCN of CMP-AC  434 ) of neural engine circuit  314  receive  620  input data (e.g., each operation circuit receives a segment  408  of data). The plurality of operation circuits performs  635  a portion of a parallel sorting operation on the input data. Accumulator  414  receives and stores  645  second results of the parallel sorting operations. The second results include the compared data values and tagged indices indicating the sorted order of the data values. 
     Embodiments of the process described above with reference to  FIG. 6  are merely illustrative. Moreover, sequence of the proves may be modified or omitted. 
     Example Parallel Sorting Network 
       FIG. 7  is a conceptual diagram illustrating an example parallel sorting network  700  performed at neural processor circuit  218 , according to one embodiment. The parallel sorting network  700  illustrated in  FIG. 7  is a bitonic sorting network that receives  16  data values  702  and simultaneously produces a maximum value  770  and a minimum value  780  of input data at the end of the sorting operation. 
     In the example of  FIG. 7 , the parallel sorting network  700  is embodied using one or more neural engine circuits  314 . Multiple operation circuits (e.g., OC 0  through OCN) in the one or more neural engine circuits  314  perform sorting (comparison) operations represented by arrows in  FIG. 7 . Task  710 , task  720 , task  725 , task  730 , task  733 , task  735 , task  740 , task  741 , task  743 , and task  745  are performed in sequence to accomplish a parallel sorting operation. Each of these tasks may be performed by one or more neural engine circuits  314 . 
     Sorting operations of task  710  may be performed in a single cycle of one neural engine circuit  314  by having 8 of its operation circuits perform the sorting operations in parallel. Alternatively, the same operation may be performed in multiple cycles by using a fewer number of operation circuits in one neural engine circuits  314 . If the number of sorting operations is large, operation circuits in two or more neural engine circuits  314  may be operated in parallel. 
     After task  710  is finished, one or more neural engine circuits  314  are updated to perform task  720  and execute the sorting operations as defined by task  720 . After tasks  720 ,  725 ,  730 ,  733 ,  735 ,  740 ,  741 ,  743  and  745 , the indices tagged to the data values indicate the sorted order of the data values. A data value with an index indicating the highest number is output as maximum number  770 , and another data value with an index indicating the lowest number is output as minimum number  780 . 
     In alternative embodiments (not shown in  FIG. 7 ), input data may include multiple elements (e.g., 16, 100, 256 elements, etc.). Each element may be an individual data value (e.g., individual elements in a vector), a vector, or multiple vectors that undergo sorting operations by the neural engines  314 . 
     While particular embodiments and applications have been illustrated and described, it is to be understood that the invention is not limited to the precise construction and components disclosed herein and that various modifications, changes and variations which will be apparent to those skilled in the art may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope of the present disclosure.