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US Patent 9646243 Convolutional neural networks using resistive processing unit array

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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
96462431
Patent Inventor Names
Tayfun Gokmen1
Date of Patent
May 9, 2017
1
Patent Application Number
152626061
Date Filed
September 12, 2016
1
Patent Citations Received
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US Patent 12118329 Dual capacitor mixed signal mutiplier
2
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US Patent 12111878 Efficient processing of convolutional neural network layers using analog-memory-based hardware
3
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US Patent 12112264 Dynamic configuration of readout circuitry for different operations in analog resistive crossbar array
4
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US Patent 12112200 Pipeline parallel computing using extended memory
5
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US Patent 12112250 Lossless tiling in convolution networks—resetting overlap factor to zero at section boundaries
6
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US Patent 11687763 Method, apparatus and computer program to carry out a training procedure in a convolutional neural network
7
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US Patent 11705191 Non-volatile memory die with deep learning neural network
8
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US Patent 11769042 Reconfigurable systolic neural network engine
...
Patent Primary Examiner
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Wilbert L. Starks, Jr.
1
Patent abstract

Technical solutions are described for implementing a convolutional neural network (CNN) using resistive processing unit (RPU) array. An example method includes configuring an RPU array corresponding to a convolution layer in the CNN based on convolution kernels of the layer. The method further includes performing forward pass computations via the RPU array by transmitting voltage pulses corresponding to input data to the RPU array, and storing values corresponding to output currents from the RPU arrays as output maps. The method further includes performing backward pass computations via the RPU array by transmitting voltage pulses corresponding to error of the output maps, and storing the output currents from the RPU arrays as backward error maps. The method further includes performing update pass computations via the RPU array by transmitting voltage pulses corresponding to the input data of the convolution layer and the error of the output maps to the RPU array.

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