Efficient Hardware Implementation of Artificial Neural Networks Using Approximate Multiply-Accumulate Blocks

Abstract

In this paper, we explore efficient hardware implementation of feedforward artificial neural networks (ANNs) using approximate adders and multipliers. We also introduce an approximate multiplier with a simple structure leading to a considerable reduction in the ANN hardware complexity. Due to a large area requirement in a parallel architecture, the ANNs are implemented under the time-multiplexed architecture where computing resources are re-used in the multiply-accumulate (MAC) blocks. The efficient hardware implementation of ANNs is realized by replacing the exact adders and multipliers in the MAC blocks by the approximate ones taking into account the hardware accuracy. Experimental results show that the ANNs designed using the proposed approximate multiplier have smaller area and consume less energy than those designed using previously proposed prominent approximate multipliers. It is also observed that the use of both approximate adders and multipliers yields respectively up to a 64% and 43% reduction in energy consumption and area of the ANN design with a slight decrease in the hardware accuracy when compared to the exact adders and multipliers.