AI models for Verilog code generation reviewed in new arXiv paper

A new review paper on arXiv examines the application of large language models (LLMs) to Verilog code generation, a critical area in hardware design. The paper systematically reviews 102 research papers, categorizing LLMs used, datasets, evaluation metrics, and alignment techniques. It identifies limitations in current studies and proposes future research directions for AI-assisted hardware design. AI

IMPACT This review synthesizes current research on LLMs for hardware design, potentially guiding future development in automated circuit design.

RANK_REASON The item is a literature review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• artificial intelligence
• arXiv
• electronic design automation
• Guang Yang
• large-language models
• Register-transfer level
• software engineering
• Verilog

• paper
• product

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