Session

DescriptionRecent advancements in GPU accelerated computing platforms and machine learning (ML) based optimization techniques have led to exciting recent research progress with large speedups on many EDA algorithms fundamental to semiconductor design flows. In this session, we highlight ongoing research deploying GPUs and ML to mask synthesis, IC design automation, and PCB design at commercial EDA vendors and semiconductor design and manufacturing companies. Research into mask synthesis techniques shows the potential for GPUs to accelerate inverse lithography and for running training and inference of ML models for process modeling. In PCB layout editing, GPU-accelerated path rendering techniques can scale to millions of rendered objects with interactive responsiveness. In IC physical design, GPU-accelerated reinforcement learning for DRC fixing combined with traditional EDA optimization techniques can automate standard cell layout generation. The combination of GPUs and ML can enable large speedups and automate key EDA tasks previously seen as intractable.