Presentation
PromptV: Leveraging LLM-powered Multi-Agent Prompting for High-quality Verilog Generation
DescriptionWith the increasing design complexity and human resource constraints, large language models (LLMs) is emerged as a promising solution for electronic design automation (EDA) tasks, particularly in hardware description language (HDL) code generation. Recent advances in agentic LLMs have demonstrated remarkable capabilities in automated Verilog code generation. However, existing approaches either demand substantial computational resources or rely on LLM-assisted single-agent prompt learning techniques, which we identify for the first time as susceptible to a degeneration phenomenon—characterized by deteriorating generative performance and diminished error detection and correction capabilities. In this paper, we propose a multi-agent prompt learning framework to address these limitations and enhance code generation quality. Our key contribution is the empirical demonstration that multi-agent architectures can effectively mitigate the degeneration risk while improving code error correction capabilities, resulting in higher-quality Verilog code generation. The effectiveness of our approach is validated through comprehensive evaluations: achieving 96.4% and 96.5% pass@10 scores on VerilogEval Machine and Human benchmarks, respectively, while attaining 100% Syntax and 99.9% Functionality pass@5 metrics on the RTLLM benchmark.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby