用AI代理让不熟悉硬件语言的开发者也能高效上手。
Enabling New HDLs with Agents
- 设计专用AI代理，提升LLM对陌生硬件语言的理解能力
- 在未训练过的HDL上实现显著性能提升
- 适合初学硬件编程或需快速掌握新语言的开发者
基于大语言模型（LLMs）的智能体正在改变编程语言生态，帮助初学者学习、生成代码并优化文档流程。硬件描述语言（HDLs）用户群体较小，更易从LLM工具中获益。本文研究了将LLM应用于未充分训练的HDLs所面临的挑战与解决方案。提出HDLAgent——一种针对多种HDL知识有限的LLM优化的AI代理，显著增强现成LLMs在HDL任务中的表现。
原文摘要 · Abstract (English)
Large Language Models (LLMs) based agents are transforming the programming language landscape by facilitating learning for beginners, enabling code generation, and optimizing documentation workflows. Hardware Description Languages (HDLs), with their smaller user community, stand to benefit significantly from the application of LLMs as tools for learning new HDLs. This paper investigates the challenges and solutions of enabling LLMs for HDLs, particularly for HDLs that LLMs have not been previously trained on. This work introduces HDLAgent, an AI agent optimized for LLMs with limited knowledge of various HDLs. It significantly enhances off-the-shelf LLMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。