DASALS: Differentiable Architecture Search-Driven Approximate Logic Synthesis

Xuan Wang, Zheyu Yan, Chang Meng, Yiyu Shi, Weikang Qian

Abstract: Approximate computing is a promising computing paradigm for designing energy-efficient systems. To automatically generate approximate circuits, many local iterative approximate logic synthesis (ALS) methods have been proposed. They need to specify a particular local approximation change and apply it to modify the local structure of a circuit in each round. This will lose some global optimization opportunities, thus, degrading circuit quality. In this paper, we propose DASALS, a differentiable archltecture search-driven ALS method, to directly search the whole circuit structure to obtain the approximate circuits with better circuit quality-accuracy trade-off. DASALS is based on a proper continuous relaxation of the discrete search space of ALS and an efficient gradient descent-based search algorithm. The experimental results show that compared with a state-of-the-art method, DASALS on average reduces the area-delay product by 10.82% and mean square error by 10.93%.