DALS

Abstract

Approximate computing is an emerging paradigm for error-tolerant applications. By introducing a reasonable amount of inaccuracy, both the area and delay of a circuit can be reduced significantly. To synthesize approximate circuits automatically, many approximate logic synthesis (ALS) algorithms have been proposed. However, they mainly focus on area reduction and are not optimal in reducing the delay of the circuits. In this paper, we propose DALS, a delay-driven ALS framework. DALS works on the AND-inverter graph (AIG) representation of a circuit. It supports a wide range of approximate local changes and some commonly-used error metrics, including error rate and mean error distance. In order to select an optimal set of nodes in the AIG to apply approximate local changes, DALS establishes a critical error network (CEN) from the AIG and formulates a maximum flow problem on the CEN. Our experimental results on a wide range of benchmarks show that DALS produces approximate circuits with significantly reduced delays.