Scaling AI-driven chip design with open agent factories

IBM Research · 1 day ago · 36

IBM Research demonstrated an open-agent-factory workflow that uses LLM agents to iteratively propose, verify, and synthesize alternative hardware designs for quantum error correction decoding. It reported a 52-times speedup versus the baseline in high-level synthesis estimates while spending about $26 in inference cost, and contrasted that with $299 for a proprietary configuration under a similar 100-hypothesis budget. Lower inference costs then enabled scaling the search (e.g., 100 to 200 hypotheses) and shifting effort toward broader verified exploration rather than just producing a single best design.