Method and system for hierarchical weight-sparse convolution processing
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 plurality of filters at a convolution layer of a neural network; segmenting the input tensor into a plurality of sub-tensors and assigning the plurality of sub-tensors to a plurality of processors; generating, for each of the plurality of filters, a hierarchical bit representation of a plurality of non-zero weights in the filter, wherein the hierarchical bit representation comprises a plurality of bits indicating whether a sub-filter has at least one non-zero weight, and a plurality of key-value pairs corresponding to the plurality of non-zero weights in the filter; identifying, based on the hierarchical bit representation, one or more of the plurality of non-zero weights and corresponding input values from the assigned sub-tensor to perform multiply-and-accumulate (MAC) operations.
MOFFETT TECHNOLOGIES CO., LIMITED
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Zhibin Yong Enxu Wei
Xiao Lu Yan Wang