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US 2019/0244080 A1Published application
Neural Network Processor with On-Chip Convolution Kernel Storage
AssigneeWisconsin Alumni Research Foundation
InventorsJing Li, Jialiang Zhang
Life of this patent
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Abstract
A neural network processor architecture provides decompression circuitry that can exploit patterns of data in kernel weights of a convolutional neural net as flattened to a vector, the compression allowing reduced kernel data storage costs including on-chip-storage.
Claims15
A neural network processor operating to receive data and to classify that data comprising:
- an input register holding received data for classification;
- a codebook storage memory holding data permitting a mapping of individual codeword values to patterns of multiple kernel weight values related to a kernel of a neural network trained to provide a set of classifications;
- a codeword memory holding codeword data to provide generation of a reconstructed kernel by indexing the codebook storage memory with codeword data;
- and
- arithmetic circuitry communicating with the input register, the codebook storage memory, and the codeword memory to generate output representing a dot product between the received data and the reconstructed kernel to classify the received data according to the set of classifications.
2 more elements
A method of operating a neural network processor receiving data to classify that data and having:
- an input register holding received data for classification;
- a codebook storage memory holding data permitting a mapping of individual codeword values to patterns of multiple kernel weight values related to a kernel of a neural network trained to provide a set of classifications;
- a codeword memory holding codeword data to permit this generation of a reconstructed kernel by indexing the codebook storage memory data with the codeword data;
- and
- arithmetic circuitry communicating with the input register, the codebook storage memory, and the codeword memory to generate output representing a dot product between the received data and the reconstructed kernel to classify the received data according to the set of classifications;
- the method comprising
- (a) training a neural network having weight values;
- (b) decomposing the weight values into vectors and compressing the vectors to create a codebook mapping individual codeword values to patterns of multiple kernel weight values and corresponding codeword data allowing reconstruction of the weight values;
- (c) loading the codebook and the codeword data into the codebook storage memory and codeword memory respectively;
- (d) operating the neural network processor to receive data for classification and to generate data representing a dot product between the received data and a kernel reconstructed from data of the codebook storage memory and codeword memory to classify the received data according to the set of classifications.
7 more elements
Dependencies are inferred from the claim text.