In the past few years, domain-specific accelerators (DSAs), such as Google's Tensor Processing Unit (TPU), have shown to offer significant performance and energy efficiency over general-purpose CPUs. However, DSAs require deep hardware knowledge to achieve high performance. Unfortunately, there are far fewer hardware designers than software developers. Leveraging AI techniques to further automate chip design becomes the key to meeting the need of rapid change in software development, which is also critical for democratizing hardware design and creating next-generation energy-efficient hardware.

This workshop will bring together leading researchers from academia and industry to explore the rapidly evolving intersection of artificial intelligence, particularly autonomous agents, and chip designs. The goal is to define a research agenda and identify new directions for NSF funding in this crucial area. The workshop will feature keynote presentations, panel discussions, and invited talks from top experts in the field.

Link to the AI for Electronic Design Automation Workshop (2024) website: https://ai4eda-workshop.github.io/