US 11,144,823 B1 · Method and system for hierarchical weight-sparse convolution processing

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for hierarchical weight-sparse convolution processing are described. An exemplary method comprises: obtaining an input tensor and a filter at a convolution layer of a neural network; segmenting the filter into a plurality of sub-filters; generating a hierarchical bit representation of the filter representing a plurality of non-zero weights in the filter, wherein the hierarchical bit representation comprises a first layer, the first layer comprising a plurality of bits respectively corresponding to the plurality of sub-filters in the filter, each of the plurality of bits indicating whether the corresponding sub-filter includes at least one non-zero weight; and performing multiply-and-accumulate (MAC) operations based on the hierarchical bit representation of the filter and the input tensor.

Claims20

Dependent claims

A computer-implemented method, comprising:

obtaining an input tensor and a filter at a convolution layer of a neural network;
segmenting the filter into a plurality of sub-filters;
generating a hierarchical bit representation of the filter representing a plurality of non-zero weights in the filter, wherein the hierarchical bit representation comprises a first layer, the first layer comprising a plurality of bits respectively corresponding to the plurality of sub-filters in the filter, each of the plurality of bits indicating whether the corresponding sub-filter includes at least one non-zero weight;
and
performing multiply-and-accumulate (MAC) operations based on the hierarchical bit representation of the filter and the input tensor.

2 more elements

A system for hierarchical weight-sparse convolution processing, comprising one or more processors and one or more non-transitory computer-readable memories coupled to the one or more processors and configured with instructions executable by the one or more processors to cause the system to perform operations comprising:

obtaining an input tensor and a filter at a convolution layer of a neural network;
segmenting the filter into a plurality of sub-filters;
generating a hierarchical bit representation of the filter representing a plurality of non-zero weights in the filter, wherein the hierarchical bit representation comprises a first layer, the first layer comprising a plurality of bits respectively corresponding to the plurality of sub-filters in the filter, each of the plurality of bits indicating whether the corresponding sub-filter includes at least one non-zero weight;
and
performing multiply-and-accumulate (MAC) operations based on the hierarchical bit representation of the filter and the input tensor.

2 more elements

A non-transitory computer-readable storage medium for hierarchical weight-sparse convolution processing, configured with instructions executable by one or more processors to cause the one or more processors to perform operations comprising:

obtaining an input tensor and a filter at a convolution layer of a neural network;
segmenting the filter into a plurality of sub-filters;
generating a hierarchical bit representation of the filter representing a plurality of non-zero weights in the filter, wherein the hierarchical bit representation comprises a first layer, the first layer comprising a plurality of bits respectively corresponding to the plurality of sub-filters in the filter, each of the plurality of bits indicating whether the corresponding sub-filter includes at least one non-zero weight;
and
performing multiply-and-accumulate (MAC) operations based on the hierarchical bit representation of the filter and the input tensor.

2 more elements

Dependencies are inferred from the claim text.

Method and system for hierarchical weight-sparse convolution processing

Life of this patent

Method and system for hierarchical weight-sparse convolution processing

Life of this patent

Abstract

Claims20