Report for NSF Workshop on AI for Electronic Design Automation

2026-01-20

AI Summary

This work addresses the pressing need to enhance the efficiency and accessibility of electronic design automation (EDA), a process traditionally burdened by complexity and lengthy design cycles. For the first time, it systematically integrates large language models (LLMs), graph neural networks (GNNs), reinforcement learning, neuro-symbolic approaches, and machine learning–enhanced SAT solvers to comprehensively explore the potential of artificial intelligence across four core EDA stages: physical synthesis and manufacturing, high-level and logic-level synthesis, AI-driven optimization toolkits, and testing and verification. The study proposes a novel roadmap centered on interdisciplinary collaboration and infrastructure development, culminating in actionable recommendations for National Science Foundation (NSF) funding initiatives aimed at fostering deep integration of AI and EDA to accelerate the democratization of next-generation hardware design.

Technology Category

• Machine Learning: Hardware-aware ML
• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver alongside NeurIPS 2024. Bringing together experts across machine learning and EDA, the workshop examined how AI-spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.-can facilitate EDA and shorten design turnaround. The workshop includes four themes: (1) AI for physical synthesis and design for manufacturing (DFM), discussing challenges in physical manufacturing process and potential AI applications; (2) AI for high-level and logic-level synthesis (HLS/LLS), covering pragma insertion, program transformation, RTL code generation, etc.; (3) AI toolbox for optimization and design, discussing frontier AI developments that could potentially be applied to EDA tasks; and (4) AI for test and verification, including LLM-assisted verification tools, ML-augmented SAT solving, security/reliability challenges, etc. The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next-generation hardware systems. The workshop information can be found on the website https://ai4eda-workshop.github.io/.

Problem

• Electronic Design Automation
• Artificial Intelligence
• Design Turnaround Time
• Verification
• Physical Synthesis

Innovation

• Large Language Models
• Graph Neural Networks
• Reinforcement Learning
• Neurosymbolic Methods
• Electronic Design Automation