Massively Parallel AIG Resubstitution
Yang Sun, Tianji Liu, Martin D. F. Wong, Evangeline F. Y. Young · 2024
Resubstitution is a flexible algorithmic framework for circuit restructuring that has been incorporated into many high-effort logic optimization flows. It is thus important to speed up resubstitution in order to obtain high-quality realizations of large-scale designs. This paper proposes a massively parallel AIG resubstitution algorithm targeting GPUs, with effective approaches to addressing cyclic dependencies and restructuring conflicts. Compared with ABC and mockturtle, our algorithm achieves 41.9× and 50.3× acceleration on average without quality degradation. When combining our resubstitution with other GPU algorithms, a GPU-based resyn2rs sequence obtains 46.4× speedup over ABC with 0.8% and 5.8% smaller area and delay respectively.