EXTREME SPARSE DEEP LEARNING EDGE INFERENCE ACCELERATOR

Abstract

A neural network inference accelerator includes first and second neural processing units (NPUs) and a sparsity management unit. The first NPU receives activation and weight tensors based on an activation sparsity density and a weight sparsity density both being greater than a predetermined sparsity density. The second NPU receives activation and weight tensors based on at least one of the activation sparsity density and the weight sparsity density being less than or equal to the predetermined sparsity density. The sparsity management unit controls transfer of the activation tensor and the weight tensor based on the activation sparsity density and the weight sparsity density with respect to the predetermined sparsity density.

Description

TECHNICAL FIELD

The subject matter disclosed herein relates to neural networks. More particularly, the subject matter disclosed herein relates to an extreme-sparsity deep-learning edge inference accelerator.

BACKGROUND

Deep neural networks (DNNs) may be accelerated by NPUs (Neural Processing Units). The sparsity inside operands of General Matrix Multiply (GEMM) operations in DNNs may be exploited to further accelerate NPUs. Structured sparsity, especially N:M (N nonzero elements out of M weight values) sparsity, may be helpful to maintain accuracy and save hardware overhead compared to random sparsity. Some tensors have higher degrees of sparsity and some have moderate levels of sparsity.

SUMMARY

An example embodiment provides a neural network inference accelerator that may include a memory, a first neural processing unit, a second neural processing unit, and a sparsity management unit. The memory may be configured to store at least one activation tensor and at least one weight tensor. The first neural processing unit may be configured to receive the activation tensor and the weight tensor from the memory based on an activation sparsity density of the activation tensor and a weight sparsity density of the weight tensor corresponding to the activation tensor both being greater than a predetermined sparsity density. The second neural processing unit may be configured to receive the activation tensor and the weight tensor from the memory based on at least one of the activation sparsity density of the activation tensor and the weight sparsity density of the weight tensor corresponding to the activation tensor being less than or equal to the predetermined sparsity density. The sparsity management unit may be configured to control transfer of the activation tensor and the weight tensor corresponding to the activation tensor from the memory to the first neural processing unit or to the second neural processing system based on the activation sparsity density of the activation tensor and the weight sparsity density of the weight tensor with respect to the predetermined sparsity density. In one embodiment, the first neural processing unit may be configured to compute a first result for the activation tensor and the weight tensor, and the second neural processing unit may be configured to compute a second result for the activation tensor and the weight tensor. In another embodiment, the neural network inference accelerator may further include a compressor unit configured to receive and compress the first result computed by the first neural processing unit, and to receive and compress the second result computed by the second neural processing unit, and the memory may be further configured to store the first result compressed by the compressor unit and store the second result compressed by the compressor unit. In still another embodiment, the compressor unit may be further configured to generate first metadata associated with the first result and to generate second metadata associated with the second result, and the memory may be further configured to store the first metadata and the second metadata. In yet another embodiment, at least one of the activation tensor and the weight tensor is compressed, and the neural network inference accelerator may further include a decompressor unit that may be configured to decompress the activation tensor to the activation sparsity density based on the activation tensor being compressed, and to decompress the weight tensor to the weight sparsity density based on the weight tensor being compressed. In one embodiment, the decompressor unit may be further configured to decompress the activation tensor to the activation sparsity density using first metadata associated with the activation tensor based on the activation tensor being compressed, and to decompress the weight tensor to the weight sparsity density using second metadata associated with the weight tensor based on the weight tensor being compressed. In another embodiment, the activation sparsity density may be based on a structured-sparsity arrangement or a random-sparsity arrangement. In still another embodiment, the activation sparsity density may be based on a 1:4 structured-sparsity arrangement, or a 2:8 structured-sparsity arrangement. In yet another embodiment, the weight sparsity density may be based on a structured-sparsity arrangement or a random-sparsity arrangement. In one embodiment, the weight sparsity density may be based on a 1:4 structured-sparsity arrangement, or a 2:8 structured-sparsity arrangement.

An example embodiment provides a neural network inference accelerator that may include a decompressor unit, the first neural processing unit, a second neural processing unit, and a sparsity management unit. The decompressor unit may be configured to decompress an activation tensor to a first predetermined sparsity density based on the activation tensor being compressed, and to decompress an weight tensor to a second predetermined sparsity density based on the weight tensor being compressed. The first neural processing unit may be configured to receive the activation tensor and the weight tensor from the decompressor unit based on the first predetermined sparsity density and the second predetermined sparsity density both being greater than a predetermined sparsity density threshold. The second neural processing unit may be configured to receive the activation tensor and the weight tensor from the decompressor unit based on at least one of the first predetermined sparsity density and the second predetermined sparsity density being less than or equal to the predetermined sparsity density threshold. The sparsity management unit may be configured to control transfer of the activation tensor and the weight tensor to the first neural processing unit or to the second neural processing system based on the first predetermined sparsity density and the second predetermined sparsity density with respect to the predetermined sparsity density threshold. In one embodiment, the neural network inference accelerator may further include a memory configured to store the activation tensor and the weight tensor, and in which the decompressor unit receives the activation tensor and the weight tensor from the memory. In another embodiment, the first neural processing unit may be configured to compute a first result for the activation tensor and the weight tensor, and the second neural processing unit may be configured to compute a second result for the activation tensor and the weight tensor. In still another embodiment, the neural network inference accelerator may further include a compressor unit configured to receive and compress the first result computed by the first neural processing unit, and to receive and compress the second result computed by the second neural processing unit, and in which the memory may be further configured to store the first result compressed by the compressor unit and store the second result compressed by the compressor unit. In yet another embodiment, the compressor unit may be further configured to generate first metadata associated with the first result and to generate second metadata associated with the second result, and the memory may be further configured to store the first metadata and the second metadata. In one embodiment, the decompressor unit may be further configured to decompress the activation tensor to the first predetermined sparsity density using first metadata associated with the activation tensor based on the activation tensor being compressed, and to decompress the weight tensor to the second predetermined sparsity density using second metadata associated with the weight tensor based on the weight tensor being compressed. In another embodiment, the first predetermined sparsity density may be based on a structured-sparsity arrangement or a random-sparsity arrangement. In still another embodiment, the first predetermined sparsity density may be based on a 1:4 structured-sparsity arrangement, or a 2:8 structured-sparsity arrangement. In yet another embodiment, the second predetermined sparsity density may be based on a structured-sparsity arrangement or a random-sparsity arrangement. In one embodiment, the second predetermined sparsity density may be based on a 1:4 structured-sparsity arrangement, or a 2:8 structured-sparsity arrangement.

BRIEF DESCRIPTION OF THE DRAWING

In the following section, the aspects of the subject matter disclosed herein will be described with reference to exemplary embodiments illustrated in the figure, in which:

FIG. 1 depicts an example dot-product operation, which is commonly performed in a neural network for a deep learning inference;

FIG. 2A depicts an example array of elements, which may be activation values or weight values, arranged in an example N:M=4:8 or 2:4 structured-sparsity format in which in a defined group of M consecutive weights, there are at most N weights have a non-zero value;

FIG. 2B depicts an example of N:M=1:4 and 2:8 structured sparsity arrangements;

FIG. 3 depicts an example of a set of dense weights values being formed into an N:M structured sparse set of weight values;

FIG. 4 is a flowchart of process that may be used to generate a sparse neural network model;

FIG. 5 is a block diagram of an example embodiment of an extreme-sparsity deep-learning edge inference accelerator according to the subject matter disclosed herein;

FIG. 6A is a functional block diagram of an example embodiment of compressor unit of a compressor/decompressor unit according to the subject matter disclosed herein;

FIG. 6B is a functional block diagram of an example embodiment of a decompressor unit of a compressor/decompressor unit according to the subject matter disclosed herein;

FIG. 7A depicts an example embodiment of a reconfigurable dual-sparsity core architecture that may be a hybrid sparse core and/or an extreme sparse core according to the subject matter disclosed herein;

FIG. 7B depicts a second example embodiment of a reconfigurable dual-sparsity core that may be used for the hybrid sparse core and/or the extreme sparse core according to the subject matter disclosed herein; and

FIG. 8 depicts an electronic device that may include an extreme-sparsity deep-learning edge inference accelerator according to the subject matter disclosed herein.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the disclosure. It will be understood, however, by those skilled in the art that the disclosed aspects may be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail to not obscure the subject matter disclosed herein.

Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment disclosed herein. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” or “according to one embodiment” (or other phrases having similar import) in various places throughout this specification may not necessarily all be referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In this regard, as used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not to be construed as necessarily preferred or advantageous over other embodiments. Additionally, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Also, depending on the context of discussion herein, a singular term may include the corresponding plural forms and a plural term may include the corresponding singular form. Similarly, a hyphenated term (e.g., “two-dimensional,” “pre-determined,” “pixel-specific,” etc.) may be occasionally interchangeably used with a corresponding non-hyphenated version (e.g., “two dimensional,” “predetermined,” “pixel specific,” etc.), and a capitalized entry (e.g., “Counter Clock,” “Row Select,” “PIXOUT,” etc.) may be interchangeably used with a corresponding non-capitalized version (e.g., “counter clock,” “row select,” “pixout,” etc.). Such occasional interchangeable uses shall not be considered inconsistent with each other.

Also, depending on the context of discussion herein, a singular term may include the corresponding plural forms and a plural term may include the corresponding singular form. It is further noted that various figures (including component diagrams) shown and discussed herein are for illustrative purpose only, and are not drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, if considered appropriate, reference numerals have been repeated among the figures to indicate corresponding and/or analogous elements.

The terminology used herein is for the purpose of describing some example embodiments only and is not intended to be limiting of the claimed subject matter. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The terms “first,” “second,” etc., as used herein, are used as labels for nouns that they precede, and do not imply any type of ordering (e.g., spatial, temporal, logical, etc.) unless explicitly defined as such. Furthermore, the same reference numerals may be used across two or more figures to refer to parts, components, blocks, circuits, units, or modules having the same or similar functionality. Such usage is, however, for simplicity of illustration and ease of discussion only; it does not imply that the construction or architectural details of such components or units are the same across all embodiments or such commonly-referenced parts/modules are the only way to implement some of the example embodiments disclosed herein.

It will be understood that when an element or layer is referred to as being on, “connected to” or “coupled to” another element or layer, it can be directly on, connected or coupled to the other element or layer or intervening elements or layers may be present. In contrast, when an element is referred to as being “directly on,” “directly connected to” or “directly coupled to” another element or layer, there are no intervening elements or layers present. Like numerals refer to like elements throughout. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

The terms “first,” “second,” etc., as used herein, are used as labels for nouns that they precede, and do not imply any type of ordering (e.g., spatial, temporal, logical, etc.) unless explicitly defined as such. Furthermore, the same reference numerals may be used across two or more figures to refer to parts, components, blocks, circuits, units, or modules having the same or similar functionality. Such usage is, however, for simplicity of illustration and ease of discussion only; it does not imply that the construction or architectural details of such components or units are the same across all embodiments or such commonly-referenced parts/modules are the only way to implement some of the example embodiments disclosed herein.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this subject matter belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

As used herein, the term “module” refers to any combination of software, firmware and/or hardware configured to provide the functionality described herein in connection with a module. For example, software may be embodied as a software package, code and/or instruction set or instructions, and the term “hardware,” as used in any implementation described herein, may include, for example, singly or in any combination, an assembly, hardwired circuitry, programmable circuitry, state machine circuitry, and/or firmware that stores instructions executed by programmable circuitry. The modules may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, but not limited to, an integrated circuit (IC), system on-a-chip (SoC), an assembly, and so forth.

The subject matter disclosed herein provides an inference accelerator architecture that is capable of running inference operations on both structured sparsity and random sparsity for sparsity densities of less than 75% sparsity.

FIG. 1 depicts an example dot-product operation 100, which is commonly performed in a neural network for a deep learning inference. The dot product of a first tensor a=[a₁, a₂, . . . , a_n] and a second tensor w=[w₁, w₂, . . . , w_n] is defined as

a·w=Σ _i=1 ⁿ a _i w _i =a ₁ w ₁ +a ₂ w ₂ + . . . +a _n w _n  (1)

in which Σ denotes a summation, i is an index, and n is the dimension of the vector (tensor) space.

Referring to FIG. 1, the first tensor a may be an activation tensor 101 that may or may not be arranged in a structured-sparsity format. The second tensor w may be a weight tensor 102 that may or may not be arranged in a structured-sparsity format. A dot-product operation 103 of the first tensor a and the second tensor w is indicated at an output 104.

FIG. 2A depicts an example array of elements, which may be activation values or weight values, arranged in an example N:M=4:8 or 2:4 structured-sparsity format in which in a defined group (such as a row, not a column) of M consecutive weights, there are at most N weights have a non-zero value. The elements of the array are indicated by small squares, and the two different shades of gray of the elements represent zero-valued elements and non-zero-valued elements. The 4:8 and the 2:4 structured sparsity arrangement shown results in either shade of gray representing a zero-valued element or a non-zero-valued element. Structured sparsity is not limited to the 4:8 and the 2:4 structured-sparsity arrangements indicated in FIG. 2B, and may also include 1:4 and 2:8 structured sparsity arrangements.

FIG. 3 depicts an example of a set of dense weights values being formed into an N:M structured sparse set of weight values. The dense weights values W are depicted in an example matrix at 301 in which R is the number of output channels and C is the number of channels in a linear layer of neural network. Relative values are depicted as light and dark matrix elements (blocks) in which relatively lower-value elements are depicted as lighter gray and relatively higher-value elements are depicted as darker grays. At 302, the weight values W are grouped into 4×4 groups 303 prior to pruning. Sparse subnetwork masks for the two weight groups are indicated at 304. After pruning, the pruned weights are deployed at 305 in a N:M structured sparse arrangement in which in each group of M consecutive weights, there are at most N weights have a non-zero value. The CN/M indicated at 205 means that as only N elements out of M weights in C channels are kept, the channel size of the weight tensor shrinks from C to CN/M.

FIG. 4 is a flowchart of process 400 that may be used to generate a sparse neural network model. At 401, the model is trained using as a dense model. That is, dense weight tensors are used during dense training. After the dense model has been trained, the dense tensors may be pruned to create more Zeros in the tensors and a compression mask may be created at 402. The pruning is based on weight values below a threshold value are pruned. Other pruning techniques are also possible. The compression mask may be used for decompression of the weight tensors and as metadata for computation units. At 403, the model is retrained based on the pruned weight tensors. The overall process 400 may be repeated of training, pruning and retraining to generate a sparse neural network model.

FIG. 5 is a block diagram of an example embodiment of an extreme-sparsity deep-learning edge inference accelerator 500 according to the subject matter disclosed herein. The inference accelerator 500 may include a memory 501, a sparsity management unit 502, a scheduler 503, one or more a hybrid sparse cores 504 and one or more extreme sparse cores 505.

The memory 501 may store dense weight tensors and/or compressed weight tensors. Additionally, the memory 501 may store dense activation tensors and/or compressed activation tensors. The memory 501 may also store dense and/or compressed weight matrices, and dense and/or compressed activation matrices. In one embodiment, the memory 501 may include a compressor/decompressor unit 506. The terms “tensor” or “tensors” will be used herein for convenience, and it should be understood that the terms “matrix” or “matrices” may be also be used herein interchangeably with the terms “tensor” and “tensors.” Metadata that is associated with the compressed weight tensors may be stored in the memory 501. Similarly, metadata that is associated with the compressed activation tensors may be stored in the memory 501.

The sparsity management unit 502 may be configured to control transfer of activation and weight tensors between the memory 501, the hybrid sparse core 504 the extreme sparse core 505. In one embodiment, the sparsity management unit 502 controls where the activation and weight are transferred based on a sparsity density threshold. For example, if a sparsity density of an activation tensor and a sparsity density of a weight tensor corresponding to the activation tensor are both greater than a sparsity density threshold, such as 25% sparsity, the activation tensor and the weight tensor are transferred to the hybrid sparse core 504. Other sparsity density thresholds are possible. If at least one of the sparsity density of an activation tensor and the sparsity density of a corresponding weight tensor is less than the sparsity density threshold, the activation tensor and the weight tensor are transferred to the extreme sparse core.

The scheduler 503 may be configured to select tasks and cores based on availability of the hybrid sparse cores 504 and the extreme sparse cores 505, and the types of tasks being processed.

The hybrid sparse core 504 may be configured to process (i.e., compute) a result for an activation tensor and a corresponding weight tensor that has been transferred to the hybrid sparse core 504 based on the sparsity density of the activation tensor and the sparsity density of the weight tensor both being greater than the sparsity density threshold. Similarly, the extreme sparse core 505 may be configured (i.e., compute) a result for an activation tensor and a corresponding weight tensor that has been transferred to the extreme sparse core 505 based on at least one of the sparsity density of the activation tensor or the sparsity density of the weight tensor being less than or equal to the sparsity density threshold.

FIG. 6A is a functional block diagram of an example embodiment of compressor unit 600 of the compressor/decompressor unit 506 according to the subject matter disclosed herein. The compressor unit 600 includes a dense matrix buffer 601 that is configured to receive dense tensors. The dense tensors are input to a zero extender unit 602 that removes zero-valued elements from the tensors to generate compresses tensors that are output to a compressed tensor buffer 603. The zero extender unit 602 also generates metadata that is associated with the compressed tensors and is output to a metadata buffer 604. The contents of the compressed tensor buffer 603 and the metadata buffer 604 may be transferred to the memory 501 (FIG. 5) during operation. The different units forming the compressor unit 600 may be formed from modules and may be combined depending on design.

FIG. 6B is a functional block diagram of an example embodiment of a decompressor unit 610 of the compressor/decompressor unit 506 according to the subject matter disclosed herein. The decompressor unit 610 includes a compressed tensor buffer 611 that is configured to receive compressed tensors. Metadata associated with the compressed tensors is received by a metadata buffer 612. The compressed tensors are input to a zero injector logic 613 that injects zero-value elements based on the metadata in the metadata buffer 612. The zero injector logic 613 outputs the dense tensor to a dense tensor buffer 614. The contents of the dense tensor buffer 614 may be transferred to a hybrid sparse core 504, the extreme sparse core 505 or to the memory 501 during operation. The different units forming the decompressor unit 610 may be formed from modules and may be combined depending on design.

In one embodiment, both the hybrid sparse core 504 and the extreme sparse core 505 may use the same reconfigurable dual-sparsity core architecture 700. FIG. 7A depicts an example embodiment of a reconfigurable dual-sparsity core architecture 700 that may be a hybrid sparse core 504 and/or an extreme sparse core 505 according to the subject matter disclosed herein. The dual-sparsity core 700 may be reconfigurable for processing structured sparsity arrangements of 1:4, 2:4, 2:8 and 4:8 for both weights and activation while also being capable of processing random sparsity arrangements for both or either weights and activations. Additional details of the reconfigurable dual-sparsity core 700 may be found in U.S. Patent Application Serial No. (Attorney Docket 1535-849 and 1535-849), both of which are incorporated by reference herein.

The example embodiment of reconfigurable dual-sparsity core 700 depicted in FIG. 7A may include four multipliers that are configured in an MULT array. The multipliers in a MULT array are indicated by a block containing an X. The dual-sparsity 700 may also include four activation multiplexers that are configured in an AMUX array. The multiplexers in an AMUX array are indicated by trapizoidal shapes. The activation buffers may be configured as four four-register buffers and are arranged in an ABUF array. Each multiplexer of the AMUX array may be a 7-to-1 multiplexer. The inputs to two of the multiplexers of the AMUX array may be connected to two four-register buffers as indicated. The connections between the multiplexers of the AMUX and the registers of the ABUF may be as shown in FIG. 7A.

The architecture of the dual-sparsity core 700 may be used for structured weight sparsity arrangements of 1:4, 2:4, 2:8 and 4:8 by selective placement of activation values in the registers of the ABUF. Referring to FIG. 7A, the respective activation channels may be indexed, as indicated at the leftmost side of each dual-sparsity core 700 configuration. The channel indexing changes based on which of the four structured sparsity arrangements for which the dual-sparsity core 700 has been configured.

When the dual-sparsity core 700 is configured for a 2:8 structured weight sparsity, the connections between the ABUF, AMUX and MULT arrays are depicted above the N:M=2:8 configuration. Sixteen activation channels are each input to a corresponding ABUF array register. The AMUX array multiplexers are controlled by a controller (not shown in FIG. 7A) to select an appropriate ABUF register based, for example, on a weight zero-bit mask or weight metadata associated with the 2:8 structured weight sparsity values. When the dual-sparsity core 700 is configured for a 2:8 structured weight sparsity, the dual-sparsity core 700 is also capable of operating in a random sparsity mode of (T_w,C_w,K_w)=(3, 1, 0) in which T_w is a lookahead in time, C_w, is a lookaside in input-channel, and K_w is a lookaside in output channel and the w subscript indicates weights.

When the dual-sparsity core 700 is configured for a 1:4 structured weight sparsity, the connections between the ABUF, AMUX and MULT arrays are depicted above the N:M=1:4 configuration. The N:M=1.4 configuration is the same as the N:M=2:8 configuration. For the N:M=1:4 configuration, 16 activation channels are each input to a corresponding ABUF array register. The AMUX array multiplexers are controlled by a controller (not shown) to select an appropriate ABUF register based, for example, on a weight zero-bit mask or weight metadata associated with the 1:4 structured weight sparsity values. When the dual-sparsity core 700 is configured for a 1:4 structured weight sparsity, the dual-sparsity core 700 is also capable of operating in a random sparsity mode of (T_w,C_w,K_w)=(3, 0, 0).

When the dual-sparsity core 700 is configured for a 2:4 structured weight sparsity, the connections between the ABUF, AMUX and MULT arrays are depicted above the N:M=2:4 configuration. For the N:M=2:4 configuration, eight activation channels are each input to a corresponding ABUF array register as indicated. The AMUX array multiplexers are controlled by a controller (not shown) to select an appropriate ABUF register based, for example, on a weight zero-bit mask or weight metadata associated with the 2:4 structured weight sparsity values. When the dual-sparsity core 700 is configured for a 2:4 structured weight sparsity, the dual-sparsity core 700 is also capable of operating in a random sparsity mode of (T_w,C_w,K_w)=(1, 1, 0).

When the dual-sparsity core 700 is configured for a 4:8 structured weight sparsity, the connections between the ABUF, AMUX and MULT arrays are depicted above the N:M=4:8 configuration. For the N:M=4:8 configuration, eight activation channels are each input to a corresponding ABUF array register as indicated. More specifically, the two topmost multipliers have access to channels 1-6. The topmost multiplier has access to channels 1-5, and the next multiplier down has access to channels 2-6. Additionally, the two bottom most multipliers have access to channels 3-6, in which the third multiplier from the top has access to channels 3-7 and the bottom multiplier has access to channels 4-8. The AMUX array multiplexers are controlled by a controller (not shown) to select an appropriate ABUF register based, for example, on a weight zero-bit mask or weight metadata associated with the 4:8 structured weight sparsity values. When the dual-sparsity core 700 is configured for a 4:8 structured weight sparsity, the dual-sparsity core 700 is also capable of operating in a random sparsity mode of (T_w,C_w,K_w)=(3, 1, 0).

FIG. 7B depicts a second example embodiment of a reconfigurable dual-sparsity core 710 that may be used for the hybrid sparse core 504 and/or the extreme sparse core 505 according to the subject matter disclosed herein. The example dual-sparsity core 710 is configured for a 2:4 structured weight sparsity that includes a 2-cycle activation lookahead. Similar to the dual-sparsity core 700, the reconfigurable dual-sparsity core 710 may also be used to support 1:4, 4:8 and 2:8 structured-sparsity modes in addition to the 2:4 structured-sparsity mode.

The dual-sparsity core 710 may include a multiply and accumulate (MAC) unit having an array of four multipliers (each indicated by a block containing an X). The accumulator portion of the MAC unit includes an adder tree (indicated by a block containing a +) and an accumulator ACC. Additionally, the dual-sparsity core architecture 710 may include a weight buffer WBUF array that contains a depth of 3 weight registers WREGs for each multiplier of the MAC unit, and an activation buffer ABUF contains a depth of 6 activation registers AREGs for each multiplier of the MAC unit. An activation multiplexer AMUX may include an activation multiplexer (indicated by a trapezoidal shape) for each multiplier of the MAC unit. Although not explicitly shown, each activation multiplexer has a fan in of 9. That is, each activation multiplexer is a 9-to-1 multiplexer. A control unit (controller 701) receives an activation zero-bit mask (A-zero-bit mask) and weight metadata in order to control (ctrl) the multiplexers of the AMUX to select appropriate AREGs. In operation, a weight value in a WREG is input to a multiplier as a first input. The activation zero-bit mask and weight metadata is used to control the multiplexers of the AMUX to select an appropriate AREG in the ABUF corresponding to each weight value. The activation value in a selected AREG is input to a multiplier as a second input corresponding to first input to the multiplier. The dual-sparsity core 710 provides a speed up of ˜3× over a NPU architecture configured only for weight sparsity. Additional details of the reconfigurable dual-sparsity core 710 may be found in U.S. Patent Application Serial No. (Attorney Docket 1535-849 and 1535-849), both of which are incorporated by reference herein.

The dual-sparsity core 710 may also be used for random weight sparsity operations. That is, the example dual-sparsity core 710 is also configured for a random sparsity mode of (T_w=1,C_w=1,T_a=2). For random weight sparsity, the effective activation lookahead the dual-sparsity core 710 is 5 cycles based on the 6 AREG depth of the ABUF with a maximum speed up of 6× (typically 2×) over a NPU architecture configured for only weight sparsity. Regarding weight preprocessing of random weight sparsity, if the weight mask is updated infrequently, software-based preprocessing may be used. If the weight mask is updated frequently, then hardware-based preprocessing by adding a weight-preprocessing unit may be a better approach.

Although the example embodiment of the dual-sparsity core 710 is configured for a 2:4 structured weight sparsity that includes a 2-cycle activation lookahead, the dual-sparsity core 710 may be configured for other structured sparsity arrangements that also provide capability for processing random sparsity.

FIG. 8 depicts an electronic device 800 that may include an extreme-sparsity deep-learning edge inference accelerator according to the subject matter disclosed herein. Electronic device 800 and the various system components of electronic device 800 may be formed from one or modules. The electronic device 800 may include a controller (or CPU) 810, an input/output device 820 such as, but not limited to, a keypad, a keyboard, a display, a touch-screen display, a 2D image sensor, a 3D image sensor, a memory 830, an interface 840, a GPU 850, an imaging-processing unit 860, a neural processing unit 870, a TOF processing unit 880 that are coupled to each other through a bus 890. In one embodiment, the 2D image sensor and/or the 3D image sensor may be part of the imaging processing unit 860. In another embodiment, the 3D image sensor may be part of the TOF processing unit 880. The controller 810 may include, for example, at least one microprocessor, at least one digital signal processor, at least one microcontroller, or the like. The memory 830 may be configured to store a command code to be used by the controller 810 and/or to store a user data. In one embodiment, the neural processing unit 870 may be configured as an extreme-sparsity deep-learning edge inference accelerator 500 according to the subject matter disclosed herein.

The interface 840 may be configured to include a wireless interface that is configured to transmit data to or receive data from, for example, a wireless communication network using a RF signal. The wireless interface 840 may include, for example, an antenna. The electronic system 800 also may be used in a communication interface protocol of a communication system, such as, but not limited to, Code Division Multiple Access (CDMA), Global System for Mobile Communications (GSM), North American Digital Communications (NADC), Extended Time Division Multiple Access (E-TDMA), Wideband CDMA (WCDMA), CDMA2000, Wi-Fi, Municipal Wi-Fi (Muni Wi-Fi), Bluetooth, Digital Enhanced Cordless Telecommunications (DECT), Wireless Universal Serial Bus (Wireless USB), Fast low-latency access with seamless handoff Orthogonal Frequency Division Multiplexing (Flash-OFDM), IEEE 802.20, General Packet Radio Service (GPRS), iBurst, Wireless Broadband (WiBro), WiMAX, WiMAX-Advanced, Universal Mobile Telecommunication Service-Time Division Duplex (UMTS-TDD), High Speed Packet Access (HSPA), Evolution Data Optimized (EVDO), Long Term Evolution-Advanced (LTE-Advanced), Multichannel Multipoint Distribution Service (MMDS), Fifth-Generation Wireless (5G), Sixth-Generation Wireless (6G), and so forth.

Embodiments of the subject matter and the operations described in this specification may be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer-program instructions, encoded on computer-storage medium for execution by, or to control the operation of data-processing apparatus. Alternatively or additionally, the program instructions can be encoded on an artificially-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer-storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial-access memory array or device, or a combination thereof. Moreover, while a computer-storage medium is not a propagated signal, a computer-storage medium may be a source or destination of computer-program instructions encoded in an artificially-generated propagated signal. The computer-storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices). Additionally, the operations described in this specification may be implemented as operations performed by a data-processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.

While this specification may contain many specific implementation details, the implementation details should not be construed as limitations on the scope of any claimed subject matter, but rather be construed as descriptions of features specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Thus, particular embodiments of the subject matter have been described herein. Other embodiments are within the scope of the following claims. In some cases, the actions set forth in the claims may be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.

As will be recognized by those skilled in the art, the innovative concepts described herein may be modified and varied over a wide range of applications. Accordingly, the scope of claimed subject matter should not be limited to any of the specific exemplary teachings discussed above, but is instead defined by the following claims.

Claims

1. A neural network inference accelerator, comprising: a memory configured to store at least one activation tensor and at least one weight tensor;a first neural processing unit configured to receive the activation tensor and the weight tensor from the memory based on an activation sparsity density of the activation tensor and a weight sparsity density of the weight tensor corresponding to the activation tensor both being greater than a predetermined sparsity density;a second neural processing unit configured to receive the activation tensor and the weight tensor from the memory based on at least one of the activation sparsity density of the activation tensor and the weight sparsity density of the weight tensor corresponding to the activation tensor being less than or equal to the predetermined sparsity density; anda sparsity management unit configured to control transfer of the activation tensor and the weight tensor corresponding to the activation tensor from the memory to the first neural processing unit or to the second neural processing system based on the activation sparsity density of the activation tensor and the weight sparsity density of the weight tensor with respect to the predetermined sparsity density.

2. The neural network inference accelerator of claim 1, wherein the first neural processing unit is configured to compute a first result for the activation tensor and the weight tensor, and wherein the second neural processing unit is configured to compute a second result for the activation tensor and the weight tensor.

3. The neural network inference accelerator of claim 2, further comprising a compressor unit configured to receive and compress the first result computed by the first neural processing unit, and to receive and compress the second result computed by the second neural processing unit, and wherein the memory is further configured to store the first result compressed by the compressor unit and store the second result compressed by the compressor unit.

4. The neural network inference accelerator of claim 3, wherein the compressor unit is further configured to generate first metadata associated with the first result and to generate second metadata associated with the second result, and wherein the memory is further configured to store the first metadata and the second metadata.

5. The neural network inference accelerator of claim 1, wherein at least one of the activation tensor and the weight tensor is compressed, the neural network inference accelerator further comprising a decompressor unit configured to decompress the activation tensor to the activation sparsity density based on the activation tensor being compressed, and to decompress the weight tensor to the weight sparsity density based on the weight tensor being compressed.

6. The neural network inference accelerator of claim 5, wherein the decompressor unit is further configured to decompress the activation tensor to the activation sparsity density using first metadata associated with the activation tensor based on the activation tensor being compressed, and to decompress the weight tensor to the weight sparsity density using second metadata associated with the weight tensor based on the weight tensor being compressed.

7. The neural network inference accelerator of claim 5, wherein the activation sparsity density is based on a structured-sparsity arrangement or a random-sparsity arrangement.

8. The neural network inference accelerator of claim 7, wherein the activation sparsity density is based on a 1:4 structured-sparsity arrangement, or a 2:8 structured-sparsity arrangement.

9. The neural network inference accelerator of claim 7, wherein the weight sparsity density is based on a structured-sparsity arrangement or a random-sparsity arrangement.

10. The neural network inference accelerator of claim 9, wherein the weight sparsity density is based on a 1:4 structured-sparsity arrangement, or a 2:8 structured-sparsity arrangement.

11. A neural network inference accelerator, comprising: a decompressor unit configured to decompress an activation tensor to a first predetermined sparsity density based on the activation tensor being compressed, and to decompress an weight tensor to a second predetermined sparsity density based on the weight tensor being compressed;a first neural processing unit configured to receive the activation tensor and the weight tensor from the decompressor unit based on the first predetermined sparsity density and the second predetermined sparsity density both being greater than a predetermined sparsity density threshold;a second neural processing unit configured to receive the activation tensor and the weight tensor from the decompressor unit based on at least one of the first predetermined sparsity density and the second predetermined sparsity density being less than or equal to the predetermined sparsity density threshold; anda sparsity management unit configured to control transfer of the activation tensor and the weight tensor to the first neural processing unit or to the second neural processing system based on the first predetermined sparsity density and the second predetermined sparsity density with respect to the predetermined sparsity density threshold.

12. The neural network inference accelerator of claim 11, further comprising a memory configured to store the activation tensor and the weight tensor, and wherein the decompressor unit receives the activation tensor and the weight tensor from the memory.

13. The neural network inference accelerator of claim 12, wherein the first neural processing unit is configured to compute a first result for the activation tensor and the weight tensor, and wherein the second neural processing unit is configured to compute a second result for the activation tensor and the weight tensor.

14. The neural network inference accelerator of claim 13, further comprising a compressor unit configured to receive and compress the first result computed by the first neural processing unit, and to receive and compress the second result computed by the second neural processing unit, and wherein the memory is further configured to store the first result compressed by the compressor unit and store the second result compressed by the compressor unit.

15. The neural network inference accelerator of claim 14, wherein the compressor unit is further configured to generate first metadata associated with the first result and to generate second metadata associated with the second result, and wherein the memory is further configured to store the first metadata and the second metadata.

16. The neural network inference accelerator of claim 15, wherein the decompressor unit is further configured to decompress the activation tensor to the first predetermined sparsity density using first metadata associated with the activation tensor based on the activation tensor being compressed, and to decompress the weight tensor to the second predetermined sparsity density using second metadata associated with the weight tensor based on the weight tensor being compressed.

17. The neural network inference accelerator of claim 11, wherein the first predetermined sparsity density is based on a structured-sparsity arrangement or a random-sparsity arrangement.

18. The neural network inference accelerator of claim 17, wherein the first predetermined sparsity density is based on a 1:4 structured-sparsity arrangement, or a 2:8 structured-sparsity arrangement.

19. The neural network inference accelerator of claim 11, wherein the second predetermined sparsity density is based on a structured-sparsity arrangement or a random-sparsity arrangement.

20. The neural network inference accelerator of claim 19, wherein the second predetermined sparsity density is based on a 1:4 structured-sparsity arrangement, or a 2:8 structured-sparsity arrangement.