Enabling New HDLs with Agents

TL;DR

This paper introduces HDLAgent, an AI agent designed to enhance large language models' ability to learn and work with various hardware description languages, especially those with limited prior training data.

Contribution

The paper presents HDLAgent, a novel AI agent that improves LLM performance on HDLs with limited training data, facilitating HDL learning and development.

Findings

HDLAgent significantly improves LLM capabilities with limited HDL knowledge

Enhanced LLMs better support HDL learning and code generation

The approach benefits HDLs with small user communities

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.