Design methodology for highly accurate approximate multipliers for error resilient applications

Abstract

Approximate computing has become the paradigm shift for applications such as neural networks and image processing, where accurate computation is not needed and intends to improve area, power, and speed. New Multiplier architectures are proposed in this paper based on an algorithm which assigns the appropriate approximate compressor adaptively from existing set of compressors to improve the accuracy in the corresponding partial product column. Experimental results prove that the existing unsigned multiplier architectures have less accuracy than the proposed designs. To quantify the performance of the proposed designs they were assessed using image processing and neural network applications. From the results, it can be deduced that the proposed architectures achieve up to 43% increase in PSNR when compared to the existing designs, with up to 14.3% increase in power consumption.