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US Patent 9836691 Neural network instruction set architecture

Patent 9836691 was granted and assigned to Google on December, 2017 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Google
Google
0
Current Assignee
Google
Google
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
98366910
Patent Inventor Names
Ravi Narayanaswami0
Dong Hyuk Woo0
Harshit Khaitan0
Olivier Temam0
Date of Patent
December 5, 2017
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Patent Application Number
154556850
Date Filed
March 10, 2017
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Patent Citations Received
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US Patent 11704562 Architecture for virtual instructions
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US Patent 11709783 Tensor data distribution using grid direct-memory access (DMA) controller
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US Patent 11748599 Super-tiling in neural network processing to enable analytics at lower memory speed
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US Patent 11762793 Direct memory access architecture with multi-level multi-striding
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US Patent 11763147 Data management device for supporting high speed artificial neural network operation by using data caching based on data locality of artificial neural network
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US Patent 12079658 Detection of invalid machine-specific data types during data conversion
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US Patent 11775801 Neural processor
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US Patent 11775802 Neural processor
...
Patent Primary Examiner
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Dave Misir
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Patent abstract

A computer-implemented method that includes receiving, by a processing unit, an instruction that specifies data values for performing a tensor computation. In response to receiving the instruction, the method may include, performing, by the processing unit, the tensor computation by executing a loop nest comprising a plurality of loops, wherein a structure of the loop nest is defined based on one or more of the data values of the instruction. The tensor computation can be at least a portion of a computation of a neural network layer. The data values specified by the instruction may comprise a value that specifies a type of the neural network layer, and the structure of the loop nest can be defined at least in part by the type of the neural network layer.

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