Power Efficient Approximate Booth Multiplier

Abstract

Power consumption is an important constraint in multimedia and deep learning applications. Approximate computing offers efficient approach to reduce power consumption. In this paper, novel approximation is proposed for radix-4 booth multiplication. Approximation is introduced in partial product generation and partial product accumulation circuits. Radix-4 partial product generation and accumulation approximation is proposed which remarkably enhances the performance. The proposed approximate booth multiplier achieves 41% area reduction and 49% power reduction compared to an exact booth multiplier. Also, it has better area, power and error metrics compared to existing works on approximate multipliers. The proposed multiplier is evaluated with an image processing application-in Discrete Cosine Transform (DCT) encoding part of JPEG compression and found to perform almost similar to exact multiplication unit.