Enabling New HDLs with Agents

2024-12-31

AI Summary

Hardware Description Languages (HDLs) suffer from high learning barriers, limited community support, and poor native compatibility with mainstream large language models (LLMs), especially for niche HDLs (e.g., Chisel, SpinalHDL). Method: We propose HDLAGENT—the first lightweight AI agent framework enabling zero-shot adaptation across multiple HDLs without fine-tuning. It integrates domain-specific knowledge injection, HDL-aware syntactic prompting, multi-stage task decomposition, and tool-augmented reasoning. Contribution/Results: HDLAGENT significantly enhances the parsing, code generation, and pedagogical capabilities of general-purpose LLMs (e.g., GPT-4, Claude) on diverse HDLs. Experiments demonstrate substantial improvements in code generation accuracy and instructional effectiveness under zero-shot and few-shot settings. Crucially, it overcomes the inherent language barrier LLMs face with unseen HDLs, establishing a new paradigm for trustworthy AI applications in hardware design.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Large Language Models
• Hardware Description Languages
• Efficient Learning

Innovation

• HDLAgent
• Large Language Models
• Hardware Description Languages