Info: Zenodo’s user support line is staffed on regular business days between Dec 23 and Jan 5. Response times may be slightly longer than normal.

Published March 1, 2021 | Version v1
Journal article Open

A design methodology for approximate multipliers in convolutional neural networks: A case of MNIST

  • 1. Ritsumeikan University

Description

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.

Files

01 20306-37916-1-PB.pdf

Files (456.3 kB)

Name Size Download all
md5:035bbf1632628bf6d46c3ae8d35157c9
456.3 kB Preview Download