ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
Existing API-based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is available at generation time, rely on closed-source APIs incompatible with chip vendors' air-gapped security requirements, and cannot be trained on vendors' proprietary RTL codebases, leaving valuable internal data unused. Recent self-trained models address the deployment constraint but remain single-turn generators that overlook the critical role of verification in real industrial flows. To bridge these gaps, we present ChipMATE, the first self-trained multi-agent framework for RTL generation. Inspired by industrial practice where correctness...
| Authors | Zhongkai Yu, Yichen Lin, Chenyang Zhou et al. |
|---|---|
| Primary category | cs.MA |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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