Approximate computing is an energy-efficient computing paradigm for error-tolerant applications. To automatically synthesize approximate circuits, many iterative approximate logic synthesis (ALS) methods have been proposed. However, most of them do not consider applying multiple local approximate changes (LACs) in a single round, which can lead to a much shorter runtime. In this paper, we propose AccALS, a novel framework for Accelerating iterative ALS flows, based on simultaneous selection of multiple LACs in a single round. When selecting multiple LACs, there may exist conflicts among them. One important component of AccALS is a novel method to solve the conflicts. Another is an efficient measure for the mutual influence between two LACs. With its help, the problem of selecting multiple LACs is transformed into a maximum independent set problem to solve. The experimental results showed that compared to a state-of-the-art method, AccALS accelerates by up to 24.6× with a negligible circuit quality loss.

Approximate computing is an emerging computing paradigm to design energy-efficient systems. Many greedy approximate logic synthesis (ALS) methods have been proposed to automatically synthesize approximate circuits. They typically need to consider all local approximate changes (LACs) in each iteration of the ALS flow to select the best one, which is time-consuming. In this paper, we propose SEALS, a Sensitivity-driven Efficient ALS method to speed up a greedy ALS flow. SEALS centers around a newly proposed concept called sensitivity, which enables a fast and accurate error estimation method and an efficient method to filter out unpromising LACs. SEALS can handle any statistical error metric. The experimental results show that it outperforms a state-of-the-art ALS method in runtime by 12X to 15X without reducing circuit quality.