Autonomous Evolution of EDA Tools: Multi-Agent Self-Evolved ABC

Key points

• The development of electronic design automation (EDA) tools is labor-intensive and constrained by the complexity of heuristic design, interactions among algorithms, and the reengineering effort required for large codebases such as ABC.
• ABC, the de facto standard for logic synthesis, is difficult to evolve because its monolithic 1.2-million-line C codebase, deep interdependencies, and static heuristics make it hard to integrate new research.
• Existing LLM-based code evolution frameworks were limited to isolated functions or smaller repositories of tens of thousands of lines, and could not handle the scale and multi-objective optimization of full EDA tools.
• The self-evolution framework integrates multi-agent LLMs (Claude Sonnet 4.5) with domain expertise and uses a pre-evolution knowledge bootstrapping stage that teaches the structure of ABC and the related literature.
• The multi-agent structure assigns specialized LLM agents, Flow, Mapper, Logic Minimization, and Planning, to distinct subsystems of the ABC codebase, enabling collaborative and localized code evolution.
• The evolutionary iterative workflow includes code generation, compilation, formal equivalence checking (CEC) for correctness, distributed benchmark evaluation, and QoR feedback to refine the tool.

Paper links

External research summaries. These are not HDATF publications or measured product results.