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US Patent 10936569 Efficient and scalable computations with sparse tensors

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10936569
Patent Inventor Names
Benoit J. Meister24
Muthu Manikandan Baskaran24
Nicolas T. Vasilache24
Richard A. Lethin24
Date of Patent
March 2, 2021
Patent Application Number
13898159
Date Filed
May 20, 2013
Patent Citations Received
‌
US Patent 11544545 Structured activation based sparsity in an artificial neural network
1
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US Patent 11551028 Structured weight based sparsity in an artificial neural network
‌
US Patent 11573945 Efficient and scalable storage of sparse tensors
4
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US Patent 11461614 Data driven quantization optimization of weights and input data in an artificial neural network
‌
US Patent 11461615 System and method of memory access of multi-dimensional data
‌
US Patent 11514291 Neural network processing element incorporating compute and local memory elements
‌
US Patent 11537687 Spatial locality transform of matrices
11
‌
US Patent 11315007 Neural network scheduling mechanism
12
...
Patent Primary Examiner
‌
Jean M Corrielus
Patent abstract

In a system for storing in memory a tensor that includes at least three modes, elements of the tensor are stored in a mode-based order for improving locality of references when the elements are accessed during an operation on the tensor. To facilitate efficient data reuse in a tensor transform that includes several iterations, on a tensor that includes at least three modes, a system performs a first iteration that includes a first operation on the tensor to obtain a first intermediate result, and the first intermediate result includes a first intermediate-tensor. The first intermediate result is stored in memory, and a second iteration is performed in which a second operation on the first intermediate result accessed from the memory is performed, so as to avoid a third operation, that would be required if the first intermediate result were not accessed from the memory.

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