Benchmarking Large Language Models for Automated Verilog RTL Code Generation

Automating hardware design could obviate a significant amount of human error from the engineering process and lead to fewer errors. Verilog is a popular hardware description language to model and design digital systems, thus generating Verilog code is a critical first step. Emerging large language m...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-12
Main Authors: Thakur, Shailja, Baleegh Ahmad, Fan, Zhenxing, Hammond Pearce, Tan, Benjamin, Karri, Ramesh, Dolan-Gavitt, Brendan, Garg, Siddharth
Format: Article
Language:English
Subjects:
Automation
Digital systems
Functional analysis
Hardware description languages
Human error
Large language models
Programming languages
Textbooks
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Automating hardware design could obviate a significant amount of human error from the engineering process and lead to fewer errors. Verilog is a popular hardware description language to model and design digital systems, thus generating Verilog code is a critical first step. Emerging large language models (LLMs) are able to write high-quality code in other programming languages. In this paper, we characterize the ability of LLMs to generate useful Verilog. For this, we fine-tune pre-trained LLMs on Verilog datasets collected from GitHub and Verilog textbooks. We construct an evaluation framework comprising test-benches for functional analysis and a flow to test the syntax of Verilog code generated in response to problems of varying difficulty. Our findings show that across our problem scenarios, the fine-tuning results in LLMs more capable of producing syntactically correct code (25.9% overall). Further, when analyzing functional correctness, a fine-tuned open-source CodeGen LLM can outperform the state-of-the-art commercial Codex LLM (6.5% overall). Training/evaluation scripts and LLM checkpoints are available: https://github.com/shailja-thakur/VGen.
ISSN:2331-8422