LLM evaluation for RTL generation identifies three performance tiers with frontier models reaching high synthesis quality and reveals systematic failure differences between proprietary and open models.
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STG generates deterministic testbenches 720x faster than iterative LLM flows with higher coverage and fewer false passes, while serving as an 11x faster data curation engine with 127x less energy.
AutoPPA learns generalizable PPA optimization rules automatically via contrastive abstraction from diverse code pairs and applies them through adaptive search, outperforming manual methods and prior tools SymRTLO and RTLRewriter.
citing papers explorer
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Synthesis-in-the-Loop Evaluation of LLMs for RTL Generation: Quality, Reliability, and Failure Modes
LLM evaluation for RTL generation identifies three performance tiers with frontier models reaching high synthesis quality and reveals systematic failure differences between proprietary and open models.
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Structured Testbench Generation for LLM-Driven HDL Design and Verification-Oriented Data Curation
STG generates deterministic testbenches 720x faster than iterative LLM flows with higher coverage and fewer false passes, while serving as an 11x faster data curation engine with 127x less energy.
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AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning
AutoPPA learns generalizable PPA optimization rules automatically via contrastive abstraction from diverse code pairs and applies them through adaptive search, outperforming manual methods and prior tools SymRTLO and RTLRewriter.