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US Patent 10970619 Method and system for hierarchical weight-sparse convolution processing

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Is a
Patent
Patent
1

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
109706191
Patent Inventor Names
Enxu Yan1
Wei Wang1
Yong Lu1
Zhibin Xiao1
Date of Patent
April 6, 2021
1
Patent Application Number
169994551
Date Filed
August 21, 2020
1
Patent Citations Received
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US Patent 11556757 System and method of executing deep tensor columns in neural networks
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US Patent 11935291 Distributed sensor system
4
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US Patent 11948089 Sparse image sensing and processing
5
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US Patent 11960982 System and method of determining and executing deep tensor columns in neural networks
6
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US Patent 11962928 Programmable pixel array
7
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US Patent 11960638 Distributed sensor system
8
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US Patent 12075175 Programmable smart sensor with adaptive readout
9
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US Patent 12108141 Dynamically programmable image sensor
10
...
Patent Primary Examiner
‌
Stanley K. Hill
1
Patent 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 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.

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