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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
Takahiro Yamamoto
Ritsumeikan University
Economics, Econometrics & Finance

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A design methodology for approximate multipliers in convolutional neural networks: A case of MNIST Kenta Shirane; Takahiro Yamamoto; Hiroyuki Tomiyama
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v10.i1.pp1-10

Abstract

In this paper, we present a case study on approximate multipliers for MNIST Convolutional Neural Network (CNN). We apply approximate multipliers with different bit-width to the convolution layer in MNIST CNN, evaluate the accuracy of MNIST classification, and analyze the trade-off between approximate multiplier’s area, critical path delay and the accuracy. Based on the results of the evaluation and analysis, we propose a design methodology for approximate multipliers. The approximate multipliers consist of some partial products, which are carefully selected according to the CNN input. With this methodology, we further reduce the area and the delay of the multipliers with keeping high accuracy of the MNIST classification.
Co-Authors Hiroyuki Tomiyama Kenta Shirane