Acceleration of Convolutional Networks Using Nanoscale Memristive Devices

Document Type

Conference Proceeding

Publication Date

1-1-2018

Abstract

We discuss a convolutional neural network for handwritten digit classification and its hardware acceleration as an inference engine using nanoscale memristive devices in the spike domain. We study the impact of device programming variability on the spiking neural network’s (SNN) inference accuracy and benchmark its performance with an equivalent artificial neural network (ANN). We demonstrate optimization strategies to implement these networks with memristive devices with an on-off ratio as low as 10 and only 32 levels of resolution. Further, close to baseline accuracies can be maintained for the networks even if such memristive devices are used to duplicate the pre-determined kernel weights to enable parallel execution of the convolution operation.

Identifier

85052887581 (Scopus)

ISBN

[9783319982038]

Publication Title

Communications in Computer and Information Science

External Full Text Location

https://doi.org/10.1007/978-3-319-98204-5_20

ISSN

18650929

First Page

240

Last Page

251

Volume

893

Grant

1710009

Fund Ref

National Science Foundation

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