MAXCell: PPA-Directed Multi-Height Cell Layout Routing Optimization using Anytime MaXSAT with Constraint Learning
Jiun-Cheng Tsai, Wei-Min Hsu, Yun-Ting Hsieh, Yu-Ju Li, Wei Huang, C. N. Ho, Hsuan‐Ming Huang, Jen-Hang Yang, Heng-Liang Huang, Aaron C.-W. Liang, Charles H.‐P. Wen · 2024
To optimize power, performance, and area (PPA) of IC designs, standard cell has evolved from basic to complicated designs, resulting in complex multi-height structures. Although extensive research on single-height cell automatic synthesis, multi-height cell studies are still limited due to the extremely large solution space. In this paper, we present MAXCell, a PPA-directed standard cell layout optimization framework for both single-height and multi-height designs using anytime MaxSAT with constraint learning. This framework incorporates two novel techniques: (1) learning additional constraints from the original constraints database to accelerate convergence during problem-solving and (2) integrating a genetic algorithm with a ranking model to dynamically guide the router towards the PPA goal directly during optimization. Experimental results indicate that MAXCell outperforms previous studies that target wire length optimization, achieving a 5.5% power reduction in evaluations of 33 multi-bit flip-flop designs beyond 4nm technology. Furthermore, compared to an industrial library designed by experienced engineers, MAXCell provides a 3.5% power optimization benefit and drastically reduces the delivery time from multiple days to a mere few hours (21.6X faster). This emphasizes its efficiency and its potential in modern integrated circuit design.