Machine Learning System-Enabled GPU Acceleration for EDA
Tsung‐Wei Huang · 2021
Electronic design automation (EDA) contains many of the most challenging computational problems that optimize circuit layouts across billions of transistors. Most existing EDA algorithms are architecturally constrained by CPU parallelism, and their performances stagnate at about 8-16 cores To achieve new transformational performance milestones, new EDA algorithms must harness the power of heterogeneous parallelism comprising manycore CPUs and GPUs. However, these milestones are too difficult to achieve without a suitable software system to assist developers in the implementation complexities built into heterogeneous parallelism. Thanks to the advancement of machine learning, various software systems have been introduced to streamline the design of heterogeneous programs, and these results have inspired new heterogeneous parallel EDA solutions. In this talk, we present our research on GPU acceleration for placement and timing analysis by harnessing the power of machine learning systems. As an example, we achieve 500× speed-up for static timing analysis on a million-gate design.