Enhancing Finite State Machine Design Automation with Large Language Models and Prompt Engineering Techniques

TL;DR

This paper evaluates the effectiveness of large language models in automating finite state machine design using prompt engineering techniques, demonstrating potential improvements and broader applicability in HDL automation.

Contribution

It introduces a systematic prompt format and a novel prompt refinement method, To-do-Oriented Prompting (TOP) Patch, for enhancing LLM performance in FSM design automation.

Findings

LLMs show promise in HDL-based FSM design tasks.

Prompt engineering techniques can improve LLM success rates.

Potential for applying these methods to other domains.

Abstract

Large Language Models (LLMs) have attracted considerable attention in recent years due to their remarkable compatibility with Hardware Description Language (HDL) design. In this paper, we examine the performance of three major LLMs, Claude 3 Opus, ChatGPT-4, and ChatGPT-4o, in designing finite state machines (FSMs). By utilizing the instructional content provided by HDLBits, we evaluate the stability, limitations, and potential approaches for improving the success rates of these models. Furthermore, we explore the impact of using the prompt-refining method, To-do-Oriented Prompting (TOP) Patch, on the success rate of these LLM models in various FSM design scenarios. The results show that the systematic format prompt method and the novel prompt refinement method have the potential to be applied to other domains beyond HDL design automation, considering its possible integration with other prompt engineering techniques in the future.