Summary of Promptv: Leveraging Llm-powered Multi-agent Prompting For High-quality Verilog Generation, by Zhendong Mi and Renming Zheng and Haowen Zhong and Yue Sun and Shaoyi Huang
PromptV: Leveraging LLM-powered Multi-Agent Prompting for High-quality Verilog Generation
by Zhendong Mi, Renming Zheng, Haowen Zhong, Yue Sun, Shaoyi Huang
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Programming Languages (cs.PL); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advances in agentic Large Language Models (LLMs) have led to remarkable automated Verilog code generation capabilities. However, existing approaches either demand substantial computational resources or rely on LLM-assisted single-agent prompt learning techniques, which we observe for the first time has a degeneration issue – characterized by deteriorating generative performance and diminished error detection and correction capabilities. To address these limitations, this paper proposes a novel multi-agent prompt learning framework to enhance code generation quality. Experimental results show that the proposed method achieves 96.4% 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making computers generate code for designing electronic circuits. Right now, these computers can do a great job, but they need a lot of power or help from humans to get better. The researchers found that when these computers work alone, their skills start to decline and they make more mistakes. To fix this, the team created a new way for multiple computers to work together and improve code quality. Their method did amazingly well on tests, producing perfect or near-perfect code 96% of the time! |
Keywords
» Artificial intelligence » Prompt » Syntax
