Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The office action is in response to the application filed on August 21, 2023.
Claims 1-20 are pending and have been examined. Claims 1-20 are rejected.
Priority
Applicant's claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application claims foreign priority based on Taiwanese Patent Application No. TW112105861 filed February 17, 2023. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed July 25, 2023, and April 08, 2024, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 3, 12, and 13 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2 and 12 recite that each parallel operation module comprises “XxYxK/W multiplier modules” that perform an element-by-element multiplication on “the XxYxZ/W feature values” of a sub-feature matrix. An element-by-element multiplication requires one multiplier per operand, so the multiplier count should equal the operand count – XxYxZ/W, not XxYxK/W. Claim 1 defines Z (channel count) and K (kernel count) as independent variables with no stated relationship between them, and each parallel operation module is expressly tied to a single kernel, so there is no apparent reason its multiplier count would scale with K (total kernel count) rather than Z (the depth of the data it multiplies). It is unclear whether Z and K are intended to be equal, related by some unstated rule, or whether “K” is a drafting substitution for “Z”. A PHOSITA (person having ordinary skill in the art) cannot determine the actual number of multiplier modules, or reconcile it with the recited operand count, from the claim language.
Claims 3 and 13 depend on claims 2 and 12 respectively and reinforce the problem: they recite summing “every Z product values” generated by claim 2/12’s multiplier modules, even though claims 2 and 12 define the product count by K, not Z.
There is no sufficient basis to assume Z = K. Nothing in claim 1 or the specification explicitly states that Z equals K. Z (input channel depth) and K (kernel/output channel count) are defined as independent variables [0025]. [Figs. 4-6, 7-9] coincidentally use Z = K, but the claims are not limited to those examples, and no general rule is stated.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter), or,
Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
MPEP 2106.04(a)(2)(I) states: "The mathematical concepts grouping is defined as
mathematical relationships, mathematical formulas or equations, and mathematical
calculations."
MPEP 2106.04(a)(2)(III) states: "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental
processes include observations, evaluations, judgements, and opinions.
Further, the MPEP states: "The courts do not distinguish between mental processes that
are performed entirely in the human mind and mental processes that require a human to use a
physical aid (e.g. pen and paper or a slide run) to perform the claim limitation.
Using the two-step inquiry, claims 1-20 are each directed to non-statutory
subject matter as shown below:
Please note the following:
The following groups of claims are expressed in different statutory categories:
Claims 1-10 are directed to a method for accelerating hardware performance and convolution calculations by splitting a feature matrix into parallel chunks for simultaneous processing with the use of multiple multipliers and adder trees.
Claims 11-20 are directed to a system comprising of a machine learning optimization circuit, data dispatcher, multiplier array, and tensor combiner configured to carry out a process for providing a specialized hardware circuit designed to speed up machine learning tasks.
With respect to Claims 1 and 11, which are independent claims with identical claim limitations:
Step 1: Claim 1 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter. Claim 11 is directed to a system for providing a hardware circuit with the means to run parallel dot product operations using multiple kernels at the same time to essentially speed up machine learning math, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter.
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“generating a local feature matrix from an extraction range in a feature tensor matrix, wherein the local feature matrix comprises feature values of X columns, Y rows, and Z channels;” ; Generating a local feature matrix from an extraction range in a feature tensor range matrix is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“partitioning W sub-feature matrices from the local feature matrix, wherein each of the W sub-feature matrices comprises X×Y×Z/W feature values;”; Partitioning W sub-feature matrices from the local feature matrix is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
“simultaneously performing parallel dot product operations on the W sub-feature matrices by W×K parallel operation modules to generate W×K temporary feature matrices, wherein the W×K parallel operation modules correspond to K kernels, each of the kernels comprises weight values of M columns, N rows, and Z channels, and each of temporary feature matrices comprises X×Y/W feature values, wherein the parallel dot product operation comprises:”; Simultaneously performing parallel dot product operations on the W sub-feature matrices by WxK parallel operation modules to generate WxK temporary feature matrices is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
“each of the parallel operation modules is configured for multiplying one of the weight values of the single kernel by the X×Y×Z/W feature values, and every W parallel operation modules is configured for multiplying the one of the weight values of the single kernel by the feature values of X columns, Y rows, and Z channels;”; Multiplying one of the weight values of the single kernel by the XxYxZ/W feature values in addition to having the W parallel operation modules be configured to multiply one of the weight values of the single kernel by the feature values of the X columns, Y rows, and Z channels is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
“and integrating the W×K temporary feature matrices into a local feature output matrix corresponding to the local feature matrix, wherein the local feature output matrix comprises feature values of X columns, Y rows, and Z channels.”; Integrating the WxK temporary feature matrices into a local feature output matrix corresponding to the local feature matrix is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 1 and 11 are directed to non-statutory subject matter and rejected.
With respect to Claims 2 and 12, which have identical claim limitations and are dependent on claims 1 and 11 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“performing an element-by-element multiplication operation on the X×Y×Z/W feature values in one of the sub-feature matrices with the one of the weight values of the single kernel by the X×Y×K/W multiplier modules, so as to generate X×Y×K/W product values;” ; Performing an element-by-element multiplication operation on the X×Y×Z/W feature values in one of the sub-feature matrices with the one of the weight values of the single kernel by the X×Y×K/W multiplier modules in order to generate XxYxK/W product values is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
“and converting the X×Y×K/W product values into one of the temporary feature matrices by the adder tree module.”; Converting the XxYxK/W product values into one of the temporary feature matrices by the adder tree module is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 2 and 12 are directed to non-statutory subject matter and rejected.
With respect to Claims 3 and 13, which have identical claim limitations and are dependent on claims 2 and 12 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“wherein the adder tree module comprises X×Y/W accumulator modules, wherein the step of performing the element-by-element multiplication operation on the X×Y×Z/W feature values in the one of the sub-feature matrices with the one of the weight values of the single kernel by the X×Y×K/W multiplier modules, so as to generate the X×Y×K/W product values comprises a step of: summing every Z product values by the adder tree module to generate X×Y/W product sums,” ; Summing every Z product values by the adder tree module to generate XxY/W product sums is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“and storing the X×Y/W product sums in the X×Y/W accumulator modules in the adder tree module.”; Storing the X×Y/W product sums in the X×Y/W accumulator modules in the adder tree module is considered insignificant extra-solution activity (memory/data storage) - see MPEP 2106.05(g).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Storing the X×Y/W product sums in the X×Y/W accumulator modules in the adder tree module constitutes as storing and retrieving information in memory which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II)
Therefore, Claims 3 and 13 are directed to non-statutory subject matter and rejected.
With respect to Claims 4 and 14, which have identical claim limitations and are dependent on claims 1 and 11 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“wherein the step of integrating the W×K temporary feature matrices into the local feature output matrix corresponding to the local feature matrix comprises a step of: combining every W temporary feature matrices in sequence to generate K combination matrices, and integrating the K combination matrices into the local feature output matrix, wherein each of combination matrices comprises feature values of X columns, Y rows, and 1 channel.”; Combining every W temporary feature matrix in sequence to generate K combination matrices, and integrating the K combination matrices into the local feature output matrix is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 4 and 14 are directed to non-statutory subject matter and rejected.
With respect to Claims 5 and 15, which have identical claim limitations and are dependent on claims 1 and 11 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“generating another local feature matrix from another extraction range in the feature tensor matrix, and the other local feature matrix comprises feature values of X columns, Y rows, and Z channels;”; Generating another local feature matrix from an extraction range in the feature tensor matrix and the other local feature matrix is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“partitioning W other sub-feature matrices from the other local feature matrix, wherein each of the W other sub-feature matrices comprises X×Y×Z/W feature values;”; Partitioning W other sub-feature matrices from the other local feature matrix is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
“and simultaneously performing parallel dot product operations on the W sub-feature matrices by W×K parallel operation modules to generate W×K other temporary feature matrices, and integrating the W×K other temporary feature matrices into another local feature output matrix corresponding to the other local feature matrix, wherein the other local feature output matrix comprises feature values of X columns, Y rows, and Z channels.”; Simultaneously performing parallel dot product operations on the W sub-feature matrices by W×K parallel operation modules to generate W×K other temporary feature matrices, and integrating the W×K other temporary feature matrices into another local feature output matrix corresponding to the other local feature matrix is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 5 and 15 are directed to non-statutory subject matter and rejected.
With respect to Claims 6 and 16, which have identical claim limitations and are dependent on claims 5 and 15 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“performing an element-by-element addition operation on the local feature output matrix with the other local feature output matrix, so as to generate a combined feature output matrix.”; Performing an element-by-element addition operation on the local feature output matrix with the other local feature output matrix, so as to generate a combined feature output matrix is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 6 and 16 are directed to non-statutory subject matter and rejected.
With respect to Claims 7 and 17, which have identical claim limitations and are dependent on claims 1 and 11 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“partitioning an original matrix into a plurality of parts, and using one of the plurality of parts as the feature tensor matrix, wherein the original matrix comprises feature values of 2×(X+N-1) columns, 2×(Y+M-1) rows, and Z channels.”; Partitioning an original matrix into a plurality of parts, and using one of the plurality of parts as the feature tensor matrix is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 7 and 17 are directed to non-statutory subject matter and rejected.
With respect to Claims 8 and 18, which have identical claim limitations and are dependent on claims 1 and 11 respectively:
Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“wherein a quantity of the parallel operation modules is equal to a product of a quantity of columns of the local feature matrix and a quantity of kernels.”; Acquiring a quantity of the parallel operation modules which is equal to a product of a quantity of columns of the local feature matrix and a quantity of kernels generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The usage of parallel operation modules, feature matrices, and kernels is generally linked to a particular technological environment or field of use (hardware acceleration of neural networks/computing and neural network processors) - see MPEP 2106.05(h).
Therefore, Claims 8 and 18 are directed to non-statutory subject matter and rejected.
With respect to Claims 9 and 19, which have identical claim limitations and are dependent on claims 1 and 11 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“partitioning the local feature matrix from the extraction range in the feature tensor matrix according to an operation parameter, wherein the operational parameters comprises a column parameter, a row parameter, a kernel parameter, and a channel parameter.”; Partitioning the local feature matrix from the extraction range in the feature tensor matrix according to an operation parameter is an abstract idea of a mathematical relationship, as directed to “a mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols”. -See MPEP § 2106.04(a)(2)(I)(A).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 9 and 19 are directed to non-statutory subject matter and rejected.
With respect to Claims 10 and 20, which have identical claim limitations and are dependent on claims 9 and 19 respectively:
Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“wherein the column parameter indicates a quantity of columns in the extraction range, the row parameter indicates a quantity of rows in the extraction range, the kernel parameter indicates a quantity of the kernels, and the channel parameter indicates a quantity of channels of the extraction range.”; Possessing column, row, kernel, and channel parameters indicative of values in the extraction range generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The usage of column, row, kernel, and channel parameters indicative of values in the extraction range is generally linked to a particular technological environment or field of use (hardware acceleration of neural networks/computing and neural network processors) - see MPEP 2106.05(h).
Therefore, Claims 10 and 20 are directed to non-statutory subject matter and rejected.
Allowable Subject Matter
Claims 1-20 are subject to potential allowance. Subject matter ineligibility as a judicial exception under 35 U.S.C. $ 101 still stands.
The following is a statement of reasons for the indication of allowable subject matter: The prior art of record fails to teach or suggest, (a machine learning optimization method and circuit that partitions a local feature matrix into sub-feature matrices and processes those sub-parts simultaneously with multiple parallel operation modules, encapsulates a hardware/software flow that utilizes a data dispatcher, a multiplier array, and a tensor combiner, performs numerous dot products in parallel against kernel weights, reuses input data and weight data in order to reduce repeated movement of values through memory, uses multiplier arrays plus adder tree modules to convert product values into temporary feature matrices, temporarily stores summation results in an accumulator prior to combining them into a local output matrix, and repeats the process on neighboring extraction ranges to form the full convolution result), as a whole.
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Henry et. Al (US20230008856 A1, filed September 05, 2022) Neural Network Facilitating Fixed-Point Emulation of Floating-Point Computation: An DNN accelerator can perform fixed-point emulation of floating-point computation. In a multiplication operation on two floating-point matrices, the DNN accelerator determines an extreme exponent for a row in the first floating-point matrix and determines another extreme exponent for a column in the second floating-point matrix. The row and column can be converted to fixed-point vectors based on the extreme exponents. The two fixed-point vectors are fed into a PE array in the DNN accelerator. The PE array performs a multiplication operation on the two fixed-point vectors and generates a fixed-point inner product. The fixed-point inner product can be converted back to a floating-point inner product based on the extreme exponents. The floating-point inner product is an element in the matrix resulting from the multiplication operation on the two floating-point matrices. The matrix can be accumulated with another matrix resulting from a fixed-point emulation of a floating-point matrix multiplication.
Henry et. Al either alone or in-combination fails to disclose the claimed subject matter as a whole.
Sinha et. Al (US20210192357A1, filed May 15, 2019) Gradient adversarial training of neural networks: Systems and methods for gradient adversarial training of a neural network are disclosed. In one aspect of gradient adversarial training, an auxiliary neural network can be trained to classify a gradient tensor that is evaluated during backpropagation in a main neural network that provides a desired task output. The main neural network can serve as an adversary to the auxiliary network in addition to a standard task-based training procedure. The auxiliary neural network can pass an adversarial gradient signal back to the main neural network, which can use this signal to regularize the weight tensors in the main neural network. Gradient adversarial training of the neural network can provide improved gradient tensors in the main network. Gradient adversarial techniques can be used to train multitask networks, knowledge distillation networks, and adversarial defense networks.
Sinha et. Al either alone or in-combination fails to disclose the claimed subject matter as a whole.
Gunnam et. Al (US20210191733A1, filed December 23, 2019) Flexible Accelerator for Sparse Tensors (FAST) in Machine Learning: An apparatus includes a first tensor compute cluster configured to receive first input feature tensors, a second tensor compute cluster configured to receive second input feature tensors more sparse than the first input feature tensors, and a vector accelerator. The apparatus also includes circuitry configured to partition an input feature map into a plurality of input feature tensors based on a compression criteria and assign each of the plurality of input feature tensors to one of the first tensor compute cluster, the second tensor compute cluster, or the vector accelerator based upon at least one of parameters including a sparsity and an optimization parameter.
Gunnam et. Al either alone or in-combination fails to disclose the claimed subject matter as a whole.
Meng et. Al (US20220057993A1, filed August 21, 2020) Matrix Multiplication Engines: Vector-vector multiplication, vector-matrix multiplication and matrix-matrix multiplication are common functions performed by computing devices in artificial intelligence, machine learning, deep learning, and various other applications. Artificial intelligence as used herein refers to techniques that enable devices to mimic human intelligence, using logic, if-then rules, decision trees, and the like. Machine learning includes a subset of artificial intelligence that includes abstruse statistical techniques that enable machines to improve at tasks with experience. Deep learning includes a subset of machine learning that includes algorithms that permit software to train itself to perform tasks by exposing multilayered artificial neural networks, recurrent neural networks (RNN), convolution neural networks (CNN) or the like to vast amounts of data. For ease of explanation artificial intelligence, as used herein, also includes machine learning, deep learning and the like.
Meng et. Al either alone or in-combination fails to disclose the claimed subject matter as a whole.
Rossi et. Al (US20200310761A1, filed March 27, 2020) Hardware accelerator method, system and device: A system includes an addressable memory array, one or more processing cores, and an accelerator framework coupled to the addressable memory. The accelerator framework includes a Multiply Accumulate (MAC) hardware accelerator cluster. The MAC hardware accelerator cluster has a binary-to-residual converter, which, in operation, converts binary inputs to a residual number system. Converting a binary input to the residual number system includes a reduction modulo 2m and a reduction modulo 2m−1, where m is a positive integer. A plurality of MAC hardware accelerators performs modulo 2m multiply-and-accumulate operations and modulo 2m−1 multiply-and-accumulate operations using the converted binary input. A residual-to-binary converter generates a binary output based on the output of the MAC hardware accelerators.
Rossi et. Al either alone or in-combination fails to disclose the claimed subject matter as a whole.
Wang et. Al (US20200234099A1, filed April 07, 2020) Neural processor: A processor includes a register, a non-zero weight value selector and a multiplier. The register holds a first group of weight values and a second group of weight values. Each group of weight values includes at least one weight value, and each weight value in the first group of weight values corresponding to a weight value in the second group of weight values. The non-zero weight value selector selects a non-zero weight value from a weight value in the first group of weight values or a non-zero weight value in the second group of weight values that corresponds to the weight value in the first group of weight values. The multiplier multiplies the selected non-zero weight value and an activation value that corresponds to the selected non-zero weight value to form an output product value.
Wang et. Al either alone or in-combination fails to disclose the claimed subject matter as a whole.
Das et. Al (US20200293858A1, filed January 29, 2020) Method and apparatus for processing computation of zero value in processing of layers in neural network: A method and an apparatus for processing layers in a neural network fetch Input Feature Map (IFM) tiles of an IFM tensor and kernel tiles of a kernel tensor, perform a convolutional operation on the IFM tiles and the kernel tiles by exploiting IFM sparsity and kernel sparsity, and generate a plurality of OFM tiles corresponding to the IFM tiles.
Das et. Al either alone or in-combination fails to disclose the claimed subject matter as a whole.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOOR F CHEEMA whose telephone number is (571)272-9642. The examiner can normally be reached Monday-Friday 7:30am-5:00pm alternative Fridays off.
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/N.F.C./Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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