Enabling New HDLs with Agents
Project Overview
The document explores the innovative application of generative AI in education through the development of HDLAgent, an AI agent aimed at improving the performance of Large Language Models (LLMs) in generating code for Hardware Description Languages (HDLs) such as Chisel, PyRTL, and DSLX. Recognizing the challenges posed by limited training data for these specialized languages, HDLAgent enhances code generation accuracy by employing advanced techniques like memory integration, compiler feedback, and few-shot learning. The evaluations conducted reveal substantial improvements in success rates for HDL code generation, underscoring HDLAgent's effectiveness as a valuable educational tool. By facilitating the teaching and learning of new HDL languages, HDLAgent represents a significant advancement in harnessing generative AI to support educational outcomes, demonstrating the potential of AI technologies to enhance coding education and improve student engagement with complex programming concepts.
Key Applications
HDLAgent
Context: Educational context for students learning new Hardware Description Languages (HDLs) like Chisel, PyRTL, and DSLX.
Implementation: HDLAgent was developed to enhance LLMs by integrating compiler feedback, few-shot learning, and specific contextual prompts to improve code generation accuracy.
Outcomes: Success rates for code generation improved significantly, with metrics showing HDLAgent's ability to raise LLM performance in HDL coding tasks to over 90% for concise examples.
Challenges: Performance declines when dealing with larger or more complex HDL designs, as well as syntax and semantic errors in less common HDLs.
Implementation Barriers
Technical and Complexity Barrier
LLMs have limited knowledge and training data for niche HDLs, affecting their performance in generating accurate code. Additionally, the performance of LLMs tends to decline significantly with larger or more complex designs, leading to lower success rates.
Proposed Solutions: HDLAgent uses memory and compiler feedback to enhance LLMs' understanding and performance with these languages. Iterative enhancements and targeted training on specific HDL syntax and semantics are suggested to improve performance.
Project Team
Mark Zakharov
Researcher
Farzaneh Rabiei Kashanaki
Researcher
Jose Renau
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mark Zakharov, Farzaneh Rabiei Kashanaki, Jose Renau
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai