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

Kenta Shirane (Ritsumeikan University)
Takahiro Yamamoto (Ritsumeikan University)
Hiroyuki Tomiyama (Ritsumeikan University)

International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol 10, No 1: March 2021

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.