8 papers
ACE-RTL: When Agentic Context Evolution Meets RTL-Specialized LLMs
Chenhui Deng, Zhongzhi Yu, Guan-Ting Liu +3
Recent advances in LLMs have sparked growing interest in applying them to hardware design automation, particularly for accurate RTL code generation. Prior efforts follow two largel…
HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimization
Hongzheng Chen, Yingheng Wang, Yaohui Cai +10
While Large Language Models (LLMs) have demonstrated significant advancements in reasoning and agent-based problem-solving, current evaluation methodologies fail to adequately asse…
Learning to Debug: LLM-Organized Knowledge Trees for Solving RTL Assertion Failures
Yunsheng Bai, Haoxing Ren
Debugging is the dominant cost in modern hardware verification, where assertion failures are among the most frequent and expensive to resolve. While Large Language Models (LLMs) sh…
JARVIS: A Multi-Agent Code Assistant for High-Quality EDA Script Generation
Ghasem Pasandi, Kishor Kunal, Varun Tej +8
This paper presents JARVIS, a novel multi-agent framework that leverages Large Language Models (LLMs) and domain expertise to generate high-quality scripts for specialized Electron…
ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation
Chenhui Deng, Yun-Da Tsai, Guan-Ting Liu +2
Recent advances in large language models (LLMs) have enabled near-human performance on software coding benchmarks, but their effectiveness in RTL code generation remains limited du…
ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolation
Chenhui Deng, Yunsheng Bai, Haoxing Ren
Recent advancements in large language models (LLMs) have expanded their application across various domains, including chip design, where domain-adapted chip models like ChipNeMo ha…