CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation
CHORUS framework uses expert model blending to outperform massive LLMs in chip design verification.
Large language models (LLMs) have advanced code generation, where executable feedback provides a more reliable learning signal than textual imitation alone. Hardware verification is an important application of code generation and accounts for a substantial fraction of modern chip...
ArXiv CS.AI
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CHORUS demonstrates that smaller, specialized models can significantly outperform massive general-purpose LLMs in domain-specific tasks like hardware verification. By leveraging complementary expert behaviors through post-training, this approach offers a highly efficient path toward reliable, high-coverage automated testing.
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