A Provably Good and Practically Efficient Algorithm for Common Path Pessimism Removal in Large Designs


Common path pessimism removal (CPPR) is imperative for eliminating redundant pessimism during static timing analysis (STA). However, turning on CPPR can significantly increase the analysis runtime by 10–100× in large designs. Recent years have seen much research on im- proving the algorithmic efficiencies of CPPR, but most are architecturally constrained by either the speed-accuracy trade-off or design-specific prun- ing heuristics. In this paper, we introduce a novel CPPR algorithm that is provably good and practically efficient. We have evaluated our algorithm on large industrial designs and demonstrated promising performance over the current state-of-the-art. As an example, our algorithm outperforms the baseline by 36–135× faster when generating the top-10K post-CPPR critical paths on a million-gate design. At the extreme, our algorithm with one core is even 4–16× faster than the baseline with 8 cores.

58th ACM/IEEE Design Automation Conference (DAC) 2021
Zizheng Guo
Zizheng Guo
Ph.D. Student

I am a Ph.D. candidate at Peking University. My research interests include data structures, algorithm design and GPU acceleration for combinatorial optimization problems.