HDLAgent, Enhancing Hardware Language in the age of LLMs
summary
A toolchain called HDLAgent wraps LLMs with self-reflection, retrieval-style context and compiler grounding to iterate on generated HDL, auto-create testbenches and run simulated verification, falling back to humans when it stalls. It raises weak models’ success on combinational benchmarks and equalizes performance across niche HDLs given a few examples and ~8k context, but pipelined designs and large outputs (>~500 tokens) still fail. Code is on the HDLAgent GitHub repo and is work in progress.
AI-written summary · about
chapters (20)
- 0:00HDL agents and goals
- 1:05LLMs for chip design
- 2:52LLMs limits in hardware
- 3:57LLM failure in Python
- 4:38Self-reflection in HDL agents
- 6:46Context and grounding in HDL
- 8:12Verification and human iteration
- 9:38LLM for code and tests
- 10:41HDL agent architecture
- 11:47Benchmarking HDL agent
- 13:15Compiler feedback for LLMs
- 14:18Smaller LLMs boosted
- 15:41New HDLs with context
- 16:01Complex hardware challenges
- 17:26Compiler errors for LLMs
- 18:51Spec writing challenges
- 20:18LLM limits in planning
- 21:21Roadmap and open-source
- 22:26LLM content restrictions
- 22:53Q&A
related talks
01h 43mBuilding LLM Agents (Hackathon)
50mLet's build an AI agent
09mBuilding LLM Agents with Tool Use
36mProduction-Ready AI Agents: From LLMs to Small Language Models
35mBuilding LLM-based Agents
40mSurviving the Agentic Hype with Small Language Models
01h 03mAgents Everywhere
01h 49mAI engineering with open access LLMs that lie, curse, and steal