ACLA: An Approximate Carry-Lookahead Adder with Intelligent Carry Judgement and Correction

摘要

Approximate computing in recent times has emerged as a popular alternative to conventional computing techniques. Fault-tolerant applications in the domains of machine learning, signal processing, and computer vision have shown promising results using approximate computing. Approximations on adders and multipliers have been widely proposed in literature and innovations on that front are still a necessity so as to target specific applications. In this paper, an approximate carry-lookahead adder (ACLA) is proposed which makes use of an intelligent approach for judging the carry of subsequent stages. Also, a correction mechanism is proposed so as to hinder substantial accuracy loss. Experimental results show that ACLA is faster than the traditional ripple-carry adder by 70.5% for 32-bit configurations on an average. In terms of accuracy, for 32-bit configurations, ACLA outperforms other state-of-the-art adders such as SARA [1] and BCSA [2] by 51%.