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

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 approximat...

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Bibliographic Details
Published inInternational journal of reconfigurable and embedded systems Vol. 10; no. 1; p. 1
Main Authors Shirane, Kenta, Yamamoto, Takahiro, Tomiyama, Hiroyuki
Format Journal Article
LanguageEnglish
Published Yogyakarta IAES Institute of Advanced Engineering and Science 01.03.2021
Subjects
Accuracy
Artificial neural networks
Classification
Convolution
Critical path
Design engineering
Evaluation
Multipliers
Neural networks
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Summary: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.
Bibliography:ObjectType-Case Study-2
SourceType-Scholarly Journals-1
content type line 14
ObjectType-Report-1
ISSN:2089-4864
2722-2608
2089-4864
DOI:10.11591/ijres.v10.i1.pp1-10