Efficient Processing of Convolutional Neural Network Layers Using Analog-memory-based Hardware

Published in USPTO, 2020

Recommended citation: G. W. Burr and B. D. Killeen. 2020. Efficient Processing of Convolutional Neural Network Layers Using Analog-memory-based Hardware. 20200117986, filed March 25, 2019, and issued April 16, 2020. https://uspto.report/patent/app/20200117986

Abstract

According to one or more embodiments, a computer implemented method for implementing a convolutional neural network (CNN) using a crosspoint array includes configuring the crosspoint array corresponding to a convolution layer in the CNN by storing one or more convolution kernels of the convolution layer in one or more crosspoint devices of the crosspoint array. The method further includes performing computations for the CNN via the crosspoint array by transmitting voltage pulses corresponding to a vector of input data of the convolution layer to the crosspoint array. Performing the CNN computations further includes outputting an electric current representative of performing a multiplication operation at a crosspoint device in the crosspoint array based on a weight value stored by the crosspoint device and the voltage pulses from the input data. Performing the CNN computations further includes passing the output electric current from the crosspoint device to a selected integrator.

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