Fusion of Global Placement and Gate Sizing with Differentiable Optimization

Abstract

Gate sizing is critical in VLSI design because it significantly influences final design quality. Traditional design flows typically treat gate sizing as a separate step due to its discreteness nature. However, this approach not only undermines the optimization efforts of earlier stages like placement, but also restricts the exploration space for gate sizing. To address these challenges, we introduce an innovative design flow fusing gate sizing with the earlier global placement stage. Our method employs differentiable timing and leakage power objectives and leverages GPU-accelerated computation to enhance design quality directly and efficiently. Our experimental results demonstrate significant improvements in timing and power metrics, with an average improvement of 77.1% in total negative slack (TNS) and 43.5% in worst negative slack (WNS), and meanwhile achieving a reduction in leakage power consumption by 1% compared with one of the most popular design tools, OpenROAD. Our method can speedup the design process by up to 7×.

Publication
IEEE/ACM International Conference on Computer-Aided Design (ICCAD) 2024
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.