RefEvo achieves 95% pass rate on 20 hardware modules for SystemC reference model generation using dynamic multi-agent planning, co-evolutionary verification, and spec anchoring, with 71% token reduction.
Chatmodel: Automating reference model design and verification with llms
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ChatHLS presents a multi-agent LLM system for HLS error debugging via adaptive case expansion and QoR-aware directive optimization, reporting 32.6% better debugging than Gemini-3-pro plus speedups on kernels and accelerators.
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RefEvo: Agentic Design with Co-Evolutionary Verification for Agile Reference Model Generation
RefEvo achieves 95% pass rate on 20 hardware modules for SystemC reference model generation using dynamic multi-agent planning, co-evolutionary verification, and spec anchoring, with 71% token reduction.
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ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis
ChatHLS presents a multi-agent LLM system for HLS error debugging via adaptive case expansion and QoR-aware directive optimization, reporting 32.6% better debugging than Gemini-3-pro plus speedups on kernels and accelerators.