PromptV: Leveraging LLM-powered Multi-Agent Prompting for High-quality Verilog Generation

• URL: http://arxiv.org/abs/2412.11014v1
• Date: Sun, 15 Dec 2024 01:58:10 GMT
• Title: PromptV: Leveraging LLM-powered Multi-Agent Prompting for High-quality Verilog Generation
• Authors: Zhendong Mi, Renming Zheng, Haowen Zhong, Yue Sun, Shaoyi Huang,
• Abstract summary: This paper proposes a novel multi-agent prompt learning framework to address limitations and enhance code generation quality. We show for the first time that multi-agent architectures can effectively mitigate the degeneration risk while improving code error correction capabilities.
• Score: 9.990225157705966
• License:
• Abstract: Recent advances in agentic LLMs have demonstrated 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. This paper proposes a novel multi-agent prompt learning framework to address these limitations and enhance code generation quality. We show for the first time that multi-agent architectures can effectively mitigate the degeneration risk while improving code error correction capabilities, resulting in higher-quality Verilog code generation. Experimental results show that the proposed method could achieve 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.

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