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US Patent 9805303 Rotating data for neural network computations

Patent 9805303 was granted and assigned to Google on October, 2017 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
Google
Google
Current Assignee
Google
Google
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
9805303
Date of Patent
October 31, 2017
Patent Application Number
14845022
Date Filed
September 3, 2015
Patent Citations Received
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US Patent 11875874 Data structures with multiple read ports
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US Patent 11868804 Processor instruction dispatch configuration
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US Patent 11868908 Processor compiler for scheduling instructions to reduce execution delay due to dependencies
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US Patent 11868250 Memory design for a processor
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US Patent 11669733 Processing unit and method for computing a convolution using a hardware-implemented spiral algorithm
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US Patent 11762602 Enhanced input of machine-learning accelerator activations
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US Patent 11769042 Reconfigurable systolic neural network engine
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US Patent 11783176 Enhanced storage device memory architecture for machine learning
...
Patent Primary Examiner
‌
Kakali Chaki
Patent abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing a layer output for a convolutional neural network layer, the method comprising: receiving a plurality of activation inputs; forming a plurality of vector inputs from the plurality of activation inputs, each vector input comprising values from a distinct region within the multi-dimensional matrix; sending the plurality of vector inputs to one or more cells along a first dimension of the systolic array; generating a plurality of rotated kernel structures from each of the plurality of kernel; sending each kernel structure and each rotated kernel structure to one or more cells along a second dimension of the systolic array; causing the systolic array to generate an accumulated output based on the plurality of value inputs and the plurality of kernels; and generating the layer output from the accumulated output.

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