The recent artificial intelligence (AI) boom has been primarily driven by three confluence forces: algorithms, big data, and computing power enabled by modern integrated circuits (ICs), including specialized AI accelerators. This talk will present a closed-loop perspective for synergistic AI and agile IC design with two main themes, AI for IC and IC for AI. As semiconductor technology enters the era of extreme scaling and heterogeneous integration, IC design and manufacturing complexities become extremely high. More intelligent and agile IC design technologies are needed than ever to optimize performance, power, manufacturability, design cost, etc., and deliver equivalent scaling to Moore’s Law. This talk will present our recent results leveraging modern AI and machine learning with domain-specific customizations for agile IC design and manufacturing, including DREAMPlace (DAC’19 and TCAD’21 Best Paper Awards) and its various extensions, DARPA-funded MAGICAL for analog/mixed-signal layout automation, LithoGAN for design-technology co-optimization, etc. Meanwhile, on the IC for AI frontier, customized ICs, including those with beyond-CMOS technologies, can drastically improve AI performance and energy efficiency by orders of magnitude. I will present a set of work on hardware and software co-design for optical neural networks and photonic ICs (2021 ACM Student Research Competition Grand Finals 1st Place, etc.). Closing the virtuous cycle between AI and IC holds great potential to advance the state-of-the-art of each other significantly.

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