Design and Analysis of Multipliers for DNN application using approximate 4:2 Compressors

Abstract

The demand for Deep Learning applications in resource constrained devices is booming in recent years. Theuse of Deep Neural Network (DNN) is the leading method in these applications which has error resilient nature.This allows the use of Approximate Computing for efficient computation to leverage efficiency accuracy tradeoff by replacing Approximate Multiplier in place of exact multipliers. In this paper we proposed ApproximateCompressors and compared them in different cases of 8 bit integer dadda multipliers in terms of the Error metricsand accuracy in real life object classification applications. The approximate multipliers are designed using differentcompressors and used to perform multiplication in ResNet. We have proposed two approximate compressorsdesigns Design 1 and Design 2.The proposed 4:2 compressors design shows the more correct outputs and lessWorst Case Relative Error (WCRE) in the range of 2-16. Our proposed 4:2 compressor Design1 is utilized in themodified Reduction circuitry of dadda multiplier and shows the accuracy of 81.6 % for DNN application