PATENT DOCUMENT

Publication Number: US-11513799-B2
Application Number: US-201916673499-A
Country: US
Kind Code: B2

Title: Chained buffers in neural network processor

Abstract:
Embodiments of the present disclosure relate to chained buffers in a neural processor circuit. The neural processor circuit includes multiple neural engines, a planar engine, a buffer memory, and a flow control circuit. At least one neural engine operates as a first producer of first data or a first consumer of second data. The planar engine operates as a second consumer receiving the first data from the first producer or a second producer sending the second data to the first consumer. Data flow between the at least one neural engine and the planar engine is controlled using at least a subset of buffers in the buffer memory operating as at least one chained buffer that chains flow of the first data and the second data between the at least one neural engine and the planar engine.

Claims:
What is claimed is: 
     
       1. A neural processor circuit, comprising:
 a plurality of neural engine circuits, at least one of the neural engine circuits configured to produce first data or consume second data by performing at least convolution operations on a channel of data; 
 a planar engine circuit configured to consume the first data received from the at least one neural engine circuit or produce the second data for the at least one neural engine circuit by performing at least operations on one or more channels of data; and 
 a buffer memory coupled to the at least one neural engine circuit and the planar engine circuit, the buffer memory including a plurality of buffers, at least one of the buffers configured to control data flow between the at least one neural engine circuit and the planar engine circuit by operating as a chained buffer, wherein the chained buffer: 
 controls flow of the first data from the at least one neural engine circuit to the planar engine circuit, and 
 controls flow of the second data from the planar engine circuit to the at least one neural engine circuit. 
 
     
     
       2. The neural processor circuit of  claim 1 , wherein, responsive to a result descriptor for a producing task of the at least one neural engine circuit or the planar engine circuit being set as chained, the chained buffer is configured to:
 control data flow produced by the at least one neural engine circuit or the planar engine circuit; and 
 store the first data or the second data produced by the producing task. 
 
     
     
       3. The neural processor circuit of  claim 2 , wherein the neural processor circuit further comprises:
 a neural task manager configured to stall the producing task at the at least one neural engine circuit or the planar engine circuit responsive to storing a defined amount of the first data or the second data in the chained buffer and before starting of a consuming task of the at least one neural engine circuit or the planar engine circuit. 
 
     
     
       4. The neural processor circuit of  claim 1 , wherein, responsive to a source descriptor for a consuming task of the at least one neural engine circuit or the planar engine circuit being set as chained, the chained buffer is configured to:
 control data flow to the at least one neural engine circuit or the planar engine circuit; and 
 provide the first data or the second data to a corresponding circuit of the at least one neural engine circuit or the planar engine circuit. 
 
     
     
       5. The neural processor circuit of  claim 4 , wherein the neural processor circuit further comprises:
 a neural task manager configured to start the consuming task after starting the producing task, responsive to the corresponding circuit of the at least one neural engine circuit or the planar engine circuit not operating in relation to another task different than the consuming task. 
 
     
     
       6. The neural processor circuit of  claim 4 , wherein the neural processor circuit further comprises:
 a neural task manager configured to stall the consuming task until the producing task writes a threshold amount of the first data or the second data into the chained buffer. 
 
     
     
       7. The neural processor circuit of  claim 4 , wherein the neural processor circuit is further configured to:
 map output slices of the first data or the second data produced in the producing task and stored in the chained buffer into input slices of data for the consuming task. 
 
     
     
       8. The neural processor circuit of  claim 1 , wherein the chained buffer is sized such that sizes of tiles and patches of the first data or the second data produced by the at least one neural engine circuit or the planar engine circuit match sizes of tiles and patches of the first data or the second data for consumption by the at least one neural engine circuit or the planar engine circuit. 
     
     
       9. The neural processor circuit of  claim 1 , wherein the chained buffer is configured to simultaneously store at most a defined amount of the first data or the second data. 
     
     
       10. The neural processor circuit of  claim 1 , wherein, responsive to storing a threshold amount of the first data or the second data in the chained buffer, the chained buffer is configured to provide at least the threshold amount of the first data to the planar engine circuit or provide at least the threshold amount of the second data to the at least one neural engine circuit. 
     
     
       11. A method of operating a neural processor circuit, comprising:
 operating at least one neural engine circuit of a plurality of neural engine circuits to produce first data or consume second data by performing at least convolution operations on a channel of data; 
 operating a planar engine circuit to consume the first data received from the at least one neural engine circuit or produce the second data for the at least one neural engine circuit by performing at least operations on one or more channels of data; and 
 controlling data flow between the at least one neural engine circuit and the planar engine circuit using at least one of a plurality of buffers in a buffer memory operating as a chained buffer, wherein the chained buffer: 
 controls flow of the first data from the at least one neural engine circuit to the planar engine circuit, and 
 controls flow of the second data from the planar engine circuit to the at least one neural engine circuit. 
 
     
     
       12. The method of  claim 11 , further comprising:
 setting a result descriptor for a producing task of the at least one neural engine circuit or the planar engine circuit as chained; and 
 responsive to setting the result descriptor as chained, storing the first data or the second data produced by the producing task into the chained buffer. 
 
     
     
       13. The method of  claim 12 , further comprising:
 stalling the producing task at the at least one neural engine circuit or the planar engine circuit, responsive to storing a defined amount of the first data or the second data in the chained buffer and before starting of a consuming task of the at least one neural engine circuit or the planar engine circuit. 
 
     
     
       14. The method of  claim 11 , further comprising:
 setting a source descriptor for a consuming task of the at least one neural engine circuit or the planar engine circuit as chained; and 
 responsive to setting the source descriptor as chained, providing the first data or the second data via the chained buffer to the corresponding circuit of the at least one neural engine circuit or the planar engine circuit. 
 
     
     
       15. The method of  claim 14 , further comprising:
 starting the consuming task after starting the producing task, responsive to a corresponding circuit of the at least one neural engine circuit or the planar engine circuit is not operating in relation to another task different than the consuming task. 
 
     
     
       16. The method of  claim 14 , further comprising:
 stalling the consuming task until the producing task writes a threshold amount of the first data or the second data into the chained buffer. 
 
     
     
       17. The method of  claim 14 , further comprising:
 mapping output slices of the first data or the second data produced in the producing task and stored in the chained buffer into input slices of data for the consuming task. 
 
     
     
       18. The method of  claim 11 , further comprising:
 storing in the chained buffer simultaneously at most a defined amount of the first data or the second data; and 
 after storing a threshold amount of the first data or the second data in the chained buffer,
 providing, via the chained buffer, at least the threshold amount of the first data to the planar engine circuit, or 
 providing, via the chained buffer, at least the threshold amount of the second data to the at least one neural engine circuit. 
 
 
     
     
       19. An electronic device, comprising:
 a neural processor circuit including:
 neural engine circuits at least one of which is configured to produce first data or of consume second data by performing at least convolution operations on a channel of data, 
 a planar engine circuit configured to consume the first data received from the at least one neural engine circuit or produce the second data for the at least one neural engine circuit by performing at least operations on one or more channels of data, and 
 a buffer memory coupled to the at least one neural engine circuit and the planar engine circuit, the buffer memory including a plurality of buffers, at least one of the buffers configured to control data flow between the at least one neural engine circuit and the planar engine circuit by operating as a chained buffer, wherein the chained buffer: 
 
 controls flow of the first data from the at least one neural engine circuit to the planar engine circuit, and 
 controls flow of the second data from the planar engine circuit to the at least one neural engine circuit. 
 
     
     
       20. The electronic device of  claim 19 , further comprising a system memory external to the neural processor circuit and coupled to the neural processor circuit, the system memory configured to:
 store input data a subset of which is sent to the buffer memory at a time for processing, and 
 store one or more kernels sent to the at least one neural engine circuit for performing the at least convolution operations.

Description:
BACKGROUND 
     1. Field of the Disclosure 
     The present disclosure relates to a circuit for performing operations related to neural networks, and more specifically to chained buffers in a neural network processor. 
     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 the CPU as well as increase the overall power consumption. 
     SUMMARY 
     Embodiments relate to chained buffers in a neural processor circuit. The neural processor circuit includes multiple neural engine circuits, a planar engine circuit, a buffer memory, and a flow control circuit. The neural engine circuit operates as a first producer of first data or a first consumer of second data by performing at least convolution operations on a channel of data. The planar engine circuit operates as a second consumer receiving the first data from the first producer by performing at least operations on one or more channels of data. Alternatively, the planar engine operates as a second producer sending the second data to the first consumer by performing at least operations on one or more channels of data. The buffer memory is coupled to the at least one neural engine circuit and the planar engine circuit and includes multiple buffers. At least a subset of the buffers operating as a chained buffer controls data flow between the neural engine circuit and the planar engine circuit. 
    
    
     
       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. 4  is a block diagram of a neural engine in the neural processor circuit, according to one embodiment. 
         FIG. 5  is a block diagram of a planar engine in the neural processor circuit, according to one embodiment. 
         FIG. 6A  is a block diagram of a buffer memory in the neural processor circuit including at least one chained buffer for controlling data flow between at least one neural engine and a planar engine, according to one embodiment. 
         FIG. 6B  is a block diagram of chained buffers in the neural processor circuit for controlling data flow between accumulators of the at least one neural engine and the planar engine, according to one embodiment. 
         FIG. 7  is a flowchart illustrating a method of performing control of data flow in the neural processor circuit using chained buffers, 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 chained buffers in a neural processor circuit that includes multiple neural engine circuits and a planar engine circuit. A buffer memory is coupled to the neural engine circuit and the planar engine circuit to buffer flow of data between the neural engine circuits and the planar engine circuit. The buffer memory includes buffers that are sized to store a portion of data produced by one of the neural engine circuit and the planar engine circuit for consumption by the other of the neural engine circuit and the planar engine circuit. 
     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  216  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) chip 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 (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, 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, 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, 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, and for post-processing to generate an output data  328 , as described below in detail with reference to  FIG. 4 . 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). The operations of planar engine  340  will be discussed in further detail below with reference to  FIG. 5 . 
     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 memory  334 . Buffer memory  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 memory  334  is embodied as a non-transitory memory that can be accessed by neural engines  314  and planar engine  340 . Buffer memory  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 memory  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 memory  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 memory  334 . Also, a dataset in buffer memory  334  may be divided and sent to different engines for different operations in the next operating cycle. Two datasets in buffer memory  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 memory  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 memory  334 , and a write circuit that forwards data from buffer memory  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 memory  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. 4  is a block diagram of neural engine  314 , according to one embodiment. Neural engine  314  performs various operations to facilitate machine learning such as convolution, tensor product, and other operations may involve heavy computation. 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 circuit  414  and output circuit  424 . Neural engine  314  may include fewer components than what is illustrated in  FIG. 4  or include further components not illustrated in  FIG. 4 . 
     Input buffer circuit  402  is a circuit that 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 provided to computation core  416  via shifting, neural engine  314  can perform multiply-accumulate for different segments of input data based on a fewer number of read operations. In one or more embodiments, the data of neural processor circuit  218  includes data of difference convolution groups and/or input channels. 
     Kernel extract circuit  432  is a circuit that 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 register in multiply-add (MAD) circuits of computation core  416 . In other embodiments, kernel extract circuit  432  receives kernel data in an uncompressed format and the kernel coefficients are determined without referencing a LUT or using a mask. 
     Computation core  416  is a programmable circuit that performs computation operations. For this purpose, computation core  416  may include MAD circuits MAD 0  through MADN and a post-processor  428 . Each of MAD circuits MAD 0  through MADN 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 MAD circuits to generate a processed value  412 . 
     Accumulator circuit  414  is a memory circuit that includes accumulators  414 A through  414 M that receive and store processed values  412  from MAD circuits. The processed values stored in accumulator circuit  414  may be sent back as feedback information  419  for further multiply and add operations at MAD circuits or sent to post-processor  428  for post processing. Accumulator circuit  414  in combination with MAD circuits form a multiply-accumulator (MAC)  404 . In one or more embodiments, accumulator circuit  414  may have subunits (or batches) 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 circuit  414  is sent to the MAC circuit while data stored in a second subunit of accumulator circuit  414  is sent to post-processor  428 . 
     Post-processor  428  is a circuit that performs further processing of values  412  received from accumulator circuit  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 circuit  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., 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 circuit  414  to MAD 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 . 
     Input data is typically split into smaller pieces of data for parallel processing at multiple neural engines  314  or neural engines  314  and planar engine  340 . A set of data used for a convolution operation may be referred to as a convolution group, which can be split into multiple smaller units. The hierarchy of smaller units (segments) may be convolution groups, slices, tiles, work units, output channel groups, input channels (Cin), sub-Cins for input stride, etc. For example, a convolution group may be split into several slices; a slice may be split into several tiles; a tile may be split into several work units; and so forth. In the context of neural engine  314 , a work unit may be a segment of the input data, such as data processed by planar engine  340  or data processed a prior cycle of neural engines  314  having a size that produces output values that fit into accumulator circuit  414  of neural engine  314  during a single cycle of the computation core  416 . In one case, the size of each work unit is 256 bytes. In such embodiments, for example, work units can be shaped to one of 16×16, 32×8, 64×4, 128×2 or 256×1 datasets. In the context of planar engine  340 , a work unit may be (i) a segment of input data, (ii) data from neural engine  314  or (iii) data from a prior cycle of planar engine  340  that can be processed simultaneously at planar engine  340 . 
     Rasterizer  430  may perform the operations associated with dividing the input data into smaller units (segments) and regulate the processing of the smaller units through the MACs  404  and accumulator circuit  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 to MAC  404  and send the finished output data  328  to data buffer memory  334 . Other components of neural processor circuit  218  (e.g., kernel DMA  324 , buffer DMA  320 , buffer memory  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 in different components. 
     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 . 
     Example Planar Engine 
       FIG. 5  is a block diagram of planar engine  340 , according to one embodiment. Planar engine  340  is a circuit that is separated from the plurality of neural engines  314  and can be programmed to perform in different modes of operations. For example, planar engine  340  may operate in a pooling mode that reduces the spatial size of data, in a reduction mode that reduces the rank of a tensor, in a gain-and-bias mode that provides a single-pass addition of bias and scaling by a scale factor, and in an elementwise mode that includes elementwise operations. For this purpose, planar engine  340  may include, among other components, a first format converter  502 , a first filter  506  (also referred to herein as “multi-mode horizontal filter  506 ”), a line buffer  510 , a second filter  514  (also referred to herein as “multi-mode vertical filter  514 ”), a post-processor  518 , a second format converter  522 , and a planar engine (PE) control  530  (includes rasterizer  540 ). Planar engine  340  may include fewer components or further components not illustrated in  FIG. 5 . Each component in planar engine  340  may be embodied as a circuit or a circuit in combination with firmware or software. 
     Input data  342  of planar engine  340  may be fetched from one or more source datasets that are saved in data processor circuit  318 . If a dataset to be processed by planar engine  340  is larger than a work unit of data that can be simultaneously processed by planar engine  340 , such dataset may be segmented into multiple work units for reading as input data  342  to planar engine  340 . Depending on the mode of planar engine  340 , input data  342  may include data from one or more source datasets. The source dataset described herein refers to different data saved in neural processor circuit  218  for processing. Different components of neural processor circuit  218  may generate or transmit data that is saved in data processor circuit  318 . For example, neural engines  314 , planar engine  340  (which generated data in a previous operation cycle), and system memory  230  may generate or transmit different datasets that are saved in different memory locations of data processor circuit  318 . Various source datasets may represent different tensors. In an operation cycle of planar engine  340 , different source datasets may be fetched together as input data  342 . For example, in an elementwise mode that involves the addition of two different tensors to derive a resultant tensor, the input data  342  may include data from two different source datasets, each providing a separate tensor. In other modes, a single source dataset may provide input data  342 . For example, in a pooling mode, input data  342  may be fetched from a single source dataset. 
     First format converter  502  is a circuit that performs one or more format conversions on input data  342  in one format (e.g., a format used for storing in buffer  334 ) to another format for processing in subsequent components of planar engine  340 . Such format conversions may include, among others, the following: applying a ReLU function to one or more values of input data  342 , converting one or more values of input data  342  to their absolute values, transposing a tensor included in the sources, applying gain to one or more values of input data  342 , biasing one or more values of input data  342 , normalizing or de-normalizing one or more values of input data  342 , converting floating-point numbers to signed or unsigned numbers (or vice versa), quantizing numbers, and changing the size of a tensor such as by broadcasting a value of a tensor in one or more dimensions to expand the rank of the tensor. The converted input data  342  and unconverted input data  342  to planar engine  340  are collectively referred to herein as “a version of the input data.” 
     First filter  506  is a circuit that performs a filtering operation in one direction. For this purpose, first filter  506  may include, among other components, adders, comparators, and multipliers. The filtering performed by first filter  506  may be, for example, averaging, choosing a maximum value or choosing a minimum value. When averaging, adders are used to sum the values of input data  342  and a weighting factor may be applied to the sum using a multiplier to obtain the average as the resultant values. When selecting maximum and minimum values, the comparators may be used in place of the adders and the multipliers to select the values. 
     Line buffer  510  is a memory circuit for storing the result such as one or more intermediate data obtained from first filter  506  or second filter  514 . Line buffer  510  may store values of different lines and allows access from second filter  514  or other downstream components to fetch the intermediate data for further processing. In some modes, line buffer  510  is bypassed. Line buffer  510  may also include logic circuits to perform additional operations other than merely storing the intermediate data. For example, line buffer  510  includes adder circuits  512 , which in combination with memory component, enables line buffer  510  to function as an accumulator that aggregates data generated from the results of first filter  506  or second filter  514  to separately store aggregated data of a dimension not to be reduced. 
     Similar to first filter  506 , second filter  514  performs filtering operations but in a direction different from first filter  506 . For this purpose, second filter  514  may include, among other components, adders, comparators, and multipliers. In the pooling mode, first filter  506  performs filtering operation in a first dimension, while second filter  514  performs filtering operation in a second dimension. In other modes, first filter  506  and second filter  514  may operate differently. In a reduction mode, for example, first filter  506  performs elementwise operations while second filter  514  functions as a reduction tree to aggregate values of data. 
     Post-processor  518  is a circuit that performs further processing of values fetched from other upstream components. Post-processor  518  may include specialized circuits that are efficient at performing certain types of mathematical computations that might be inefficient to perform using a general computation circuit. Operations performed by post-processor  518  may include, among others, performing square root operations and inverse of values in a reduction mode. Post-processor  518  may be bypassed in other operation modes. 
     Second format converter  522  is a circuit that converts the results of preceding components in planar engine  340  from one format to another format for output data  344 . Such format conversions may include, among others, the following: applying a ReLU function to the results, transposing a resultant tensor, normalizing or de-normalizing one or more values of the results, and other number format conversions. Output data  344  may be stored in data processor circuit  318  as the output of neural processor circuit  218  or as inputs to other components of neural processor circuit  218  (e.g., neural engine  314 ). 
     PE control  530  is a circuit that controls operations of other components in planar engine  340  based on the operation mode of planar engine  340 . Depending on the different modes of operation, PE control  530  programs register associated with the different components in planar engine  340  so that the programmed components operate in a certain manner. The pipeline of components or connections between the components in planar engine  340  may also be reconfigured. In the pooling mode, for example, data processed at by first filter  506  may be stored in line buffer  510  and then be read by second filter  514  for further filtering. In the reduction mode, however, data is processed by first filter  506 , then processed at second filter  514  and then accumulated in line buffer  510  that is programmed as an accumulator. In the elementwise mode, line buffer  510  may be bypassed. 
     PE control  530  also includes a rasterizer  540  that tracks the current task or process loop being processed at planar engine  340 . Rasterizer  540  is a circuit that tracks units or segments of input data and/or loops for processing the input data in planar engine  340 . Rasterizer  540  may control the fetch of segments to planar engine  340  in each operation cycle and may monitor the size and rank of each segment being processed by planar engine  340 . For example, smaller segments of a dataset may be fetched as input data  342  in a raster order for processing at planar engine  340  until all segments of the source dataset are processed. In fetching the segments, rasterizer  540  monitors the coordinate of the segment in the dataset. The manner in which a dataset is segmented into input data  342  for processing at planar engine  340  may be different compared to how a dataset is segmented into input data  328  for processing at neural engines  314 . 
     The dataset for processing at planar engine  340  may be larger than the capacity of planar engine  340  that can be processed in a single operation cycle. In such case, planar engine  340  fetches different segments of the dataset as input data  342  in multiple operating cycles. The fetched segment may partly overlap with a previously fetched segment and/or a next segment to be fetched. In one embodiment, the portion of overlapping data is fetched only once and reused to reduce the time and power consumption cost of planar engine  340  in fetching data. 
     Example Chained Buffers in Neural Processor Circuit 
       FIG. 6A  is a block diagram of buffer memory  334  that includes at least one chained buffer  602  for controlling data flow between neural engines  314 A,  314 B through  314 N and planar engine  340 , according to one embodiment. Neural engine  314 A,  314 B through  314 N operates as a first producer of first data  606  or a first consumer of second data  610  by performing at least convolution operations on a channel of data (e.g., input data  322 ). Planar engine  340  operates as a second consumer receiving first data  608  from the first producer or as a second producer sending second data  612  to the first consumer by performing at least operations on one or more channels of data. For any given instance of chained buffer  602 , there is a single producer and a single consumer. Thus, for any instance of chained buffer  602 , either the flow of first data  606 ,  608  is active or the flow of second data  612 ,  610  is active. 
     Buffer memory  334  is coupled to neural engines  314 A,  314 B through  314 N and planar engine  340 . Buffer memory  334  includes multiple buffers, e.g., buffers  602 ,  604  for storage of data used by neural engines  314 A,  314 B through  314 N and planar engine  340 . At least one buffer  602  of buffer memory  334  can be configured to operate as a chained buffer. Each chained buffer  602  of buffer memory  334  controls data flow between a single producer and a single consumer. Chained buffer  602  allows an output of one execution circuit (e.g., a circuit of neural engine  314 , which may operate as a producer) to be directly chained to an input of another execution circuit (e.g., a circuit of planar engine  340 , which may operate as a consumer). Other buffers  604  can be also set to operate as one or more chained buffers to chain data flow between neural engines  314 A,  314 B through  314 N and planar engine  340 . Chained buffer  602  is an ephemeral buffer, which means that after the last of data from the producer has been read by the consumer, a buffer space associated with chained buffer  602  is released and can be used by one or more subsequent tasks for other purposes, e.g., either as another ephemeral buffer or to create a new persistent buffer that would be retained for the one or more subsequent tasks. Chained buffer  602  is configured as a source chained consumer&#39;s buffer and as a destination chained producer&#39;s buffer. 
     At least a subset of other buffers  604  can be set to operate as, e.g., a non-resident buffer, a resident buffer and/or a cached buffer. The non-resident buffer is an ephemeral buffer that may be attached to, e.g., data processor DMA  320 . The non-resident buffer may be sized to hold an input or output tile (e.g., for neural engine  314 ) or an input or output patch for planar engine  340 . The non-resident buffer of a source type may be between a read port of data processor DMA  320  and neural engine  314  (or planar engine  340 ). The non-resident buffer of a destination type may be between neural engine  314  (or planar engine  340 ) and a write port of data processor DMA  320 . The resident buffer is a full-sized surface retained in buffer memory  334 , which means that the resident buffer is a persistent buffer. The resident buffer may be set as a resident destination that may create, e.g., a tensor from output data  328  of neural engine  314  or from output data  344  of planar engine  340 . The resident destination buffer may be then used as an input (e.g., a resident source) for neural engine  314  or planar engine  340 . The cached buffer is a persistent buffer. The cached buffer may have the same layout as the resident buffer, but the cached buffer may be also chained to, e.g., data processor DMA  320 . The cached buffer may be utilized when a source resident in data processor DMA  320  is retained in buffer memory  334  for use by subsequent operations, or if a destination needs to be retained in buffer memory  334  for use as a subsequent source while also producing a copy in system memory  230 . A chained destination cached buffer creates, e.g., a tensor from output data  228  of neural engine  314  (or from output data  344  of planar engine  340 ), and writes the tensor to data processor DMA  320 . The chained destination cached buffer may write a second copy of the tensor in, e.g., system memory  230 . A chained source cached buffer may read, e.g., a tensor of data from data processor DMA  320  into an execution circuit of neural engine  314  (e.g., as input data  322 ) or into planar engine  340  (e.g., as input data  344 ). The chained source cached buffer may retain a (resident) copy of the tensor of data. 
     All four buffer types in buffer memory  334  (e.g., chained, non-resident, resident and cached buffers) can be utilized for control of data flow. The ephemeral buffers control data flow between producers and consumers. Cached buffers control data flow with data processor DMA  320 , e.g., similar to non-resident buffers. Resident buffers may optionally be marked as dependent, in which case the resident buffers control data flow in a manner similar to chained buffers. The differences are that a dependent resident buffer represents a full tensor allocation, and that the producer and consumer do not need to execute at the same time (as opposed to chained buffer  602  that acts as a FIFO between a pair of execution units). The primary distinction between ephemeral buffers and persistent buffers is the allocation. A persistent buffer represents a full tensor buffer. A producer (e.g., neural engine  314 ) and consumer (e.g., planar engine  340 ) interfaced via the persistent buffer do not need to run simultaneously, and a resident copy may be retained for an arbitrary amount of time and re-used as necessary for one or more subsequent tasks. An ephemeral buffer (e.g., chained buffer  602 ) is a buffer that stores a windowed subset of a tensor. Chained buffer  602  thus acts as a FIFO between a producer (e.g., neural engine  314 ) and a consumer (e.g., planar engine  340 ), and holds a portion of the tensor. The producer and consumer necessarily are executing at the same time, and either producer or consumer can stall the other by producing or consuming data below, e.g., a threshold rate. 
     Resident buffers in buffer memory  334  may be rewritten to cached buffers automatically by, e.g., a context-switch mechanism. The context-switch is a mechanism by which a type of a buffer is switched from one type to another different type, e.g., from a resident buffer to a cached buffer. The context switch mechanism may rewrite resident buffers in buffer memory  334  to become cached buffers. On a context switch-out during a task, a resident destination may be rewritten to become a cached destination, causing an output of neural engine  314  (or planar engine  340 ) to be copied into, e.g., system memory  230 . On a subsequent context switch-in of the task, the first use of a resident source buffer is re-written to become a cached source, causing the external copy to be read in from system memory  230  (and the resident buffer may be restored for one or more subsequent tasks, if any). 
     Buffer memory  334  can include at most one chained buffer  602  corresponding to a data chain between a pair of execution circuits (e.g., a producer-consumer pair). In addition to chained buffer  602 , buffer memory  334  may include (e.g., within other buffers  604 ) up to two non-resident buffers for the read and write DMA used as corresponding inputs to a producer (e.g., an execution circuit of neural engine  314 ) and outputs from a consumer (e.g., an execution circuit of planar engine  340 ). These non-resident buffers are also ephemeral, and can be released on the last usage. Other buffers  604  in buffer memory  334  may include any number of persistent (e.g., resident) buffers, which are managed by a software and retain previously computed or read tensors (e.g., from data processor DMA  320 ) for re-use in one or more subsequent tasks (e.g., by neural engine  314  or planar engine  340 ). 
     Usage of different buffers in buffer memory  334  may be configured by source and result parameters of each task. A task has an operation type (e.g., “neural engine convolution operation”, “planar engine element-wise operation”, etc.), a result descriptor, and at least one source descriptor (e.g., element-wise operations may require a second source). The source and result descriptors may contain: a buffer type, a buffer base address (e.g., provided by the software), an indication about buffer strides, and dependency information. The indication about buffer strides may be provided by, e.g., the software so that neural engine  314  (or planar engine  340 ) can utilize appropriate elements in a tensor (e.g., in input data  322  or in input data  342 ). Flow control circuit  332  may utilize the dependency information to determine which other tasks a particular buffer in buffer memory  334  (e.g., chained buffer  602 ) may be dependent on, if any. A buffer in buffer memory  334  (e.g., chained buffer  602 ) may have a true dependency (e.g., as in the case of the dependent resident buffer) or be an alias (e.g., a reallocation of the buffer for a different purpose). Aliases may be tagged to ensure that a new unrelated use (e.g., task) of a previously allocated region does not start before the previous use finishes. 
     As discussed, data flow between neural engines  314 A,  314 B through  314 N and planar engine  340  may be controlled using at least a subset of buffers in buffer memory  334  configured as chained buffer  602 . Buffer  602  may be configured (e.g., by a software) to operate as a chained buffer that chains flow of first data  606 ,  608  and second data  610 ,  612  between neural engines  314 A,  314 B through  314 N and planar engine  340 . Chained buffer  602  is sized such that sizes of tiles and patches of first data  606  or second data  612  from a corresponding producer of the first and second producers match sizes of tiles and patches of first data  608  or second data  610  for a corresponding consumer of the first and second consumers. 
     Chained buffer  602  may be configured to simultaneously store at most a defined amount of first data  606  or second data  612 . After storing a threshold amount of first data  606  in chained buffer  602 , chained buffer  602  may provide at least the threshold amount of first data  606  as first data  608  to planar engine  340  that operates as the second consumer. Similarly, after storing a threshold amount of second data  612  in chained buffer  602 , chained buffer  602  may provide the threshold amount of second data  612  as second data  610  to neural engines  314 A,  314 B through  314 N. 
     Chaining is represented by two paired tasks, e.g., by a producing task and a consuming task. The producing task includes a set of operations performed by the first producer or the second producer. Similarly, the consuming task includes a set of operations performed by the first consumer or the second consumer. To set up a chain, the producing task needs to start with a result descriptor of the producing task written as chained. The software may set the result descriptor of the producing task as chained. One or more source descriptors of the producing task can be set (e.g., by the software) to any of other non-chained buffer types. After the result descriptor of the producing task is set as chained, buffer  602  is configured as a chained buffer to control data flow produced by the first producer or the second producer, and chained buffer  602  stores first data  606  or second data  612  produced by the producing task. As the result descriptor of the producing task is set as chained, the producer task may start executing, e.g., read data from at least one source, perform computations and then start writing into chained buffer  602 . The neural task manager  310  may stall the producing task at the first producer or the second producer after storing a defined amount of first data  606  or second data  612  in chained buffer  602 , if the consuming task of the first consumer or the second consumer has not yet started consuming data produced by the producing task as chained buffer  602  reached its space limit. The producing task is addressed (e.g., by neural task manager  310 ) to an execution circuit of the first consumer (e.g., neural engine  314 ) or to an execution circuit of the second consumer (e.g., planar engine  340 ). 
     After the producing task has started, a second task (e.g., the consuming task) is introduced, with one of its source descriptors written as chained (e.g., by the software). The consuming task is addressed (e.g., by neural task manager  310 ) to an execution circuit of the first consumer (e.g., neural engine  314 ) or to an execution circuit of the second consumer (e.g., planar engine  340 ). The software may set a source descriptor for the consuming task as chained. After the source descriptor of the consuming task is set as chained, buffer  602  is configured as a chained buffer to control data flow from the first producer to the first consumer or from the second producer to the second consumer. For example, if the consuming task is an element-wise operation on planar engine  340 , chained buffer  602  provides data for an execution circuit of planar engine  340  performing the element-wise operation. If the consuming task starts before the producing task has produced a threshold amount of data to chained buffer  602 , neural task manager  310  may stall the consuming task. Alternatively, if the consuming task starts after the producing task has produced the threshold amount of data to chained buffer  602 , the first consumer or the second consumer may immediately read and use data, and therefore freeing space for the producing task. 
     Data flow continues through chained buffer  602  with both producing and consuming tasks running. At some point in time, the first producer or the second producer finishes writing into chained buffer  602 , and a corresponding execution circuit of the first producer or the second producer would be freed up to start a new producing task. The consuming side of the chain may still be operating on remaining data from the first producer or the second producer. Once the first consumer or the second consumer finishes using the remaining data, the chain would end. In one or more embodiments, the consuming task starts before the producing task. In such case, the first consumer or the second consumer immediately stalls (e.g., by neural task manager  310 ) until the producing task has started and produced a defined amount of data to chained buffer  602  produced by an execution circuit of the first producer or the second producer. 
     From a scheduling and flow-control perspective, neural task manager  310  issues tasks to their appropriate execution circuits of neural engine  314  or planar engine  340 . Flow-control circuit  332  may overlook at a current task on a corresponding execution circuit of neural engine  314  (or planar engine  340 ). Flow-control circuit  332  may then decide on an interface-by-interface basis (e.g., neural engine write, neural engine read, planar engine write, planar engine source 1 read, planar engine source 2 read, DMA write, DMA read) whether a request for a corresponding data flow is allowed to proceed based on its buffer type and its relationship (if any) to some other interface. For example, a non-resident source read for planar engine  340  may have a consumer relationship with a read interface of data processor DMA  320  (which acts as a producer). At any point in time, there may be up to three different flow controls, e.g., one chained (or dependent) flow control and two non-resident (e.g., read and write) flow controls. In an embodiment, three simultaneously active flow controls can be e.g., flow control from a read interface of data processor DMA  320  to a read interface of planar engine  340  (non-resident read flow control), from a write interface of planar engine  340  to a read interface of neural engine  314  (chained flow control), and from a write interface of neural engine  314  to a write interface of data processor DMA  320  (non-resident write flow control). 
     The producing task and the consuming task issue (e.g., by neural task manager  310 ) to their individual execution circuits and start as soon as that execution circuit is free of other preceding tasks. The consuming task may be initiated (e.g., by neural task manager  310 ) responsive to a corresponding circuit (to which the consuming task has been issued) of the first consumer or the second consumer is not operating in relation to another task different than the consuming task. The producing task and the consuming task may start in either order. In one or more embodiments, the consuming task may perform certain operations even before the producer&#39;s task issues (e.g., a dual-source task of planar engine  340  may read some data from a non-chained second source before stalling on a chained buffer). Responsive to setting the source descriptor for the consuming task as chained, chained buffer  602  provides first data  608  or second data  610  to the corresponding circuit of the first consumer or the second consumer. Neural task manager  310  may stall the consuming task until the producing task writes a threshold amount of first data  606  or second data  612  into buffer  602 . 
     As discussed, chained tasks (e.g., the paired producing task and consuming task) are executed simultaneously. The paired producing and consuming tasks cannot not be split by the context switch mechanism because chained buffer  602  controlling data flow of the producing and consuming tasks is not a context switchable buffer. Buffers  602  dedicated for chained pairs of producing and consuming tasks (e.g., chained buffers  602 ) are simultaneously resident in buffer memory  334 , and there are no resource dependencies between any two paired producing and consuming tasks. 
     Buffer pointers to chained buffer  602  that chains data flow for a pair of producing-consuming tasks are set (e.g., by the software) to be the same. Additionally, data parameters (e.g., weight, height, depth, channel, groups, format, etc.) for the pair of producing-consuming tasks are also set to be the same for chained buffer  602 . A single task cannot be associated with more than one chained buffer  602 , which means that a chain provided via chained buffer  602  is associated with a single producer-consumer pair. Other buffers  604  in buffer memory  334  that are not chained may be configured as dependent resident buffers to allow, e.g., fully pipelined arbitrary length chains. From a flow-control perspective, dependent resident buffer  604  and chained buffer  602  operate in the same manner. The only difference between dependent resident buffer  604  and chained buffer  602  is their size. Chained buffer  602  does not contain a full tensor, and hence can back-pressure the first producer (e.g., neural engine  314 ) or the second producer (e.g., planar engine  340 ), while dependent resident buffer  604  is large enough to hold all of the produced data. 
     In one embodiment, both paired producing and consuming tasks are single-slice tasks, which means that the producing and consuming tasks operate on first data  606  and second data  612  that represent a single slice of data. If either producing or consuming task is sliced, e.g., either first data  606  or second data  612  include multiple slices of data, chained buffer  602  is configured to chain data flow such that both the producing and consuming tasks are sliced. Thus, first data  608  provided to the second consumer (e.g., planar engine  340 ) and second data  610  provided to the first consumer (e.g., neural engine  314 ) are also divided into slices. 
     In one embodiment, the consuming task does not have a vertical kernel support, which means that a kernel height, Kh, is equal to 1. In such case, input slices for the consuming task do not overlap. Hence, flow control circuit  332  can map output slices of first data  606  or second data  612  produced in the producing task and stored in chained buffer  602  into input slices of first data  608  or second data  610  for the consuming task. The producing task and the consuming task operate on a same slice at the same time (e.g., the first data  606 ,  608  belong to the same slice, and the second data  612 ,  610  belong to the same slice), with a windowed portion of a tensor sliding along the slice. At the end of the slice, the first producer (e.g., neural engine  314 ) or the second producer (e.g., planar engine  340 ) is configured to wait for the first consumer (e.g., planar engine  340 ) or the second consumer (e.g., neural engine  314 ) to finish consuming the slice before the first producer or the second producer can start writing data for a next slice (e.g., data  606  or data  612 ) into chained buffer  602 . Flow control circuit  332  prevents this data hazard by stalling the first producer (e.g., neural engine  314 ) or the second producer (e.g., planar engine  340 ) until the first consumer or the second consumer finishes consuming the data slice (e.g., slice of data  608  or slice of data  610 ). 
     In case of a convolution performed by neural engine  314  on source tensors (e.g., input data  322 ) that are too large for storage in buffer memory  334 , the convolution can be split into two or more sub-convolutions applied on two or more vertical slices of the source tensors (e.g., sub-tensors of input data  322 ). The sub-convolutions can be treated as individual convolutions of the sliced portions of their sub-tensors. Neural engine  314  performs the sub-convolutions on an input tensor (e.g., input data  322 ) split into two or more vertical input slices using kernel data  326  to produce an output tensor (e.g., output data  328 ) split into two or more output slices produced by the sub-convolutions that fit into buffer memory  334 . 
     In the case of convolution or pooling with Kh&gt;1, the consumer&#39;s input slices (e.g., slices of input data  322 ) overlap. If chained buffer  602  chains data flow between a producer (e.g., planar engine  340 ) and a consumer (e.g., neural engine  314 ) with Kh&gt;1, then the producer&#39;s input slices (e.g., slices of input data  342 ) overlap well as the consumer&#39;s input slices (e.g., slices of input data  322 ). Since the consumer&#39;s input slices represent the producer&#39;s output slices, the overlapping of the consumer&#39;s input slices is related to overwork in addition to over-fetching. To address this, an overlapping portion of chained buffer  602  between each consumer&#39;s input slice stored in chained buffer  602  is re-computed. In the case of chaining between the producer (e.g., planar engine  340 ) and the consumer (e.g., neural engine  314 ), both the producer and the consumer run its own rasterizer, e.g., rasterizer  540  and rasterizer  430 . The consumer&#39;s rasterizer (e.g., rasterizer  430 ) may operate in the same manner as if there is no chaining. The producer&#39;s rasterizer (e.g., rasterizer  540 ) is configured to back up each consumer&#39;s input slice (which is a producer&#39;s output slice, e.g., slice of input data  322 ) by an amount of overwork, e.g., the re-computed overlapping portion of the input slice determined by an overlap parameter. The software may compute a value of the overlap parameter using parameters of a consumer&#39;s convolution, e.g., the overlap parameter may be computed as a function of parameters Kh and Sy (stride factor in vertical direction). In some embodiments, the value of overlap parameter is directly proportional to Kh and Sy, and the value of overlap parameter increases with increasing Kh and decreases with increasing Sy. 
       FIG. 6B  is a block diagram of chained buffers  602 A through  602 M in buffer memory  334  interfacing neural engine  314  and planar engine  340 , according to one embodiment. Neural engine  314  includes multiple accumulators  414 A through  414 M that store processed values  412  related to, e.g., a convolution operation on a channel of input data  408 . Each accumulator  414 A through  414 M may be configured to operate as a first producer of data, e.g., corresponding data  606 A through  606 M. Further, one or more circuits of neural engine  314  (e.g., input buffer circuit  402 ) may be configured to operate as a first consumer of data (e.g., data  610 A through  610 M). 
     Planar engine  340  performs operations on one or more channels of data. In one embodiment, planar engine  340  is configured to operate as a second consumer receiving first data  608 A through  608 M from accumulators  414 A through  414 M. In another embodiment, planar engine  340  is configured to operate as a second producer sending second data  612 A trough  612 M to one or more circuits of neural engine  314 , e.g., to input buffer circuit  402 . 
     Buffer memory  334  interfaces accumulators  414 A through  414 M of neural engine  314  and planar engine  340 . Buffer memory  334  includes multiple chained buffers  602 A through  602 M. Each chained buffer  602 A through  602 M is set to chain flow of data between a corresponding accumulator  414 A through  414 M in neural engine  314  and planar engine  340 . 
     In one embodiment, each chained buffer  602 A through  602 M receives and stores corresponding first data  606 A through  606 M from a corresponding accumulator  414 A through  414 M operating as a first producer. Each chained buffer  602 A through  602 M chains flow of received and stored first data  606 A through  606 M to planar engine  340  as corresponding first data  608 A through  608 M. Planar engine  340  operates as a second consumer that uses first data  608 A through  608 M performing operations on one or more channels of corresponding first data  608 A through  608 M. 
     In another embodiment, planar engine  340  operates as a second producer of data, e.g., second data  612 A through  612 M produced by performing at least operations on one or more channels of data. Each chained buffer  602 A through  602 M receives and stores corresponding second data  612 A through  612 M from planar engine  340 . Each chained buffer  602 A through  602 M chains flow of received and stored second data  612 A through  612 M to one or more circuits of neural engine  314  (e.g., input buffer circuit  402 ) as corresponding second data  610 A through  610 M. For any given instance of chained buffer  602 A through  602 M, there is a single producer and a single consumer. Thus, for any instance of chained buffer  602 A through  602 M, either the corresponding flow of first data  606 A through  606 M,  608 A through  608 M is active, or the corresponding flow of second data  612 A through  612 M,  610 A through  610 M is active. 
     Example Process at Neural Engine Architecture 
       FIG. 7  is a flowchart illustrating a method of controlling data flow in a neural processor circuit using chained buffers, according to one embodiment. The neural processor circuit operates  702  a neural engine circuit (e.g., neural engine  314 ) as a first producer of first data or a first consumer of second data by performing at least convolution operations on a channel of data. 
     The neural processor circuit operates  704  a planar engine circuit (e.g., planar engine  340 ) as a second consumer receiving the first data from the first producer or a second producer sending the second data to the first consumer by performing at least operations on one or more channels of data. The neural processor circuit controls  706  data flow between the neural engine circuit and the planar engine circuit using at least a subset of buffers (e.g., buffers  602 ,  604 ) operating as a chained buffer (e.g., one or more chained buffers  602 ) that chains flow of the first data and the second data between the neural engine circuit and the planar engine circuit. The buffers are included in a buffer memory (e.g., buffer memory  334 ) coupled to the neural engine circuit and the planar engine circuit. 
     Embodiments of the process as described above with reference to  FIG. 7  are merely illustrative. Moreover, sequence of the process may be modified or omitted. 
     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.

Metadata:
Filing Date: 20191104
Publication Date: 20221129
Grant Date: 20221129
Priority Date: 20191104
Inventors: MILLS, CHRISTOPHER L.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06N3/044", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N3/048", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N3/045", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/544", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F9/4881", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/063", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/084", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/3004", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N20/10", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N3/063", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/063", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/3004", "inventive": true, "first": true, "tree": "[]"}]
Family ID: 75689010