Enabling New HDLs with Agents
This work addresses the problem of enabling LLM-based agents for HDLs, which could benefit the smaller HDL user community, but it appears incremental as it builds on existing LLM agent frameworks.
The paper tackles the challenge of applying Large Language Models (LLMs) to Hardware Description Languages (HDLs) that they have not been trained on, introducing HDLAgent to enhance off-the-shelf LLMs for these languages.
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.