Summary of Rtl-repo: a Benchmark For Evaluating Llms on Large-scale Rtl Design Projects, by Ahmed Allam et al.
RTL-Repo: A Benchmark for Evaluating LLMs on Large-Scale RTL Design Projects
by Ahmed Allam, Mohamed Shalan
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a benchmark called RTL-Repo, designed to evaluate large language models (LLMs) on register transfer level (RTL) design tasks. The benchmark consists of over 4000 Verilog code samples extracted from public GitHub repositories, providing the full context of each repository. Several state-of-the-art models, including GPT-4, GPT-3.5, Starcoder2, and VeriGen, are evaluated on RTL-Repo, with a focus on generating Verilog code for complex projects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special test called RTL-Repo to help machines learn how to design computer chips. It takes over 4000 pieces of code from the internet and makes sure each one has all the information needed to understand it. The researchers tested different machine learning models on this test, like GPT-4 and Starcoder2, to see how well they could make new chip designs. |
Keywords
» Artificial intelligence » Gpt » Machine learning
