ChipCraftBrain: Multi-Agent RTL Generation Framework
ChipCraftBrain is an innovative framework designed for the automated generation of Register-Transfer Level (RTL) that integrates symbolic-neural reasoning with adaptive multi-agent orchestration. It overcomes the shortcomings of current LLM-based methods, which typically achieve functional correctness rates of only 60-65% in single-shot generation, and up to 95.9% with multi-agent techniques like MAGE on VerilogEval. However, these methods struggle with more challenging industrial benchmarks, such as NVIDIA's CVDP, and lack synthesis awareness. ChipCraftBrain features four significant advancements: adaptive orchestration utilizing six specialized agents through a PPO policy on a 168-dimensional state, a hybrid symbolic-neural architecture for algorithmic solutions to K-map and truth-table issues, and knowledge-augmented generation. This framework aims to enhance correctness, synthesis awareness, and cost efficiency. The research is available on arXiv with the identifier 2604.19856.
Key facts
- ChipCraftBrain is a framework for automated RTL generation.
- It combines symbolic-neural reasoning with adaptive multi-agent orchestration.
- Single-shot LLM generation achieves only 60-65% functional correctness.
- Multi-agent approach MAGE reaches 95.9% on VerilogEval.
- Existing methods are untested on NVIDIA's CVDP benchmark.
- ChipCraftBrain uses six specialized agents orchestrated via PPO policy.
- The state space is 168-dimensional.
- An alternative world-model MPC planner is also evaluated.
- Hybrid architecture solves K-map and truth-table problems algorithmically.
- Specialized agents handle waveform timing and general RTL.
- The framework includes knowledge-augmented generation.
- Published on arXiv as 2604.19856.
Entities
Institutions
- arXiv
- NVIDIA