ACSAM: Accuracy-configurable Segmentation-based Approximate Multiplier for Error-resilient Edge-AI Applications

Abstract

Approximate computing has introduced a paradigm shift in hardware-optimized implementation of Edge-AI applications by balancing accuracy and area constraints simultaneously. In this work, a hardware-efficient, segmentation-based 16-bit approximate multiplier, ACSAM is presented for resource-constrained applications with error tolerance capabilities. The proposed 16-bit multiplier integrates various combinations of conventional and proposed 8-bit approximate multipliers with unique shifting and rounding strategies. The proposed multiplier ACSAM achieved upto 18.9% improvement in LUT utilisation on FPGA and upto 2.85× reduction in power and upto 12.27% reduction in area for ASIC implementation on 65nm technology node compared to the state-of-the-art works. ACSAM is validated using an image-blurring application to demonstrate its suitability for DSP and image-processing tasks. Additionally, FPGA and ASIC evaluations confirm its adaptability across diverse implementation requirements. © 2009-2012 IEEE.