A Multi-Expert Large Language Model Architecture for Verilog Code Generation

• URL: http://arxiv.org/abs/2404.08029v1
• Date: Thu, 11 Apr 2024 16:58:29 GMT
• Title: A Multi-Expert Large Language Model Architecture for Verilog Code Generation
• Authors: Bardia Nadimi, Hao Zheng,
• Abstract summary: This paper introduces an innovative multi-expert LLM architecture for Verilog code generation (MEV-LLM) Our architecture uniquely integrates multiple LLMs, each specifically fine-tuned with a dataset that is categorized with respect to a distinct level of design complexity. Empirical evidence from experiments highlights notable improvements in terms of the percentage of generated Verilog outputs that are syntactically and functionally correct.
• Score: 5.159745269633967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, there has been a surging interest in using large language models (LLMs) for Verilog code generation. However, the existing approaches are limited in terms of the quality of the generated Verilog code. To address such limitations, this paper introduces an innovative multi-expert LLM architecture for Verilog code generation (MEV-LLM). Our architecture uniquely integrates multiple LLMs, each specifically fine-tuned with a dataset that is categorized with respect to a distinct level of design complexity. It allows more targeted learning, directly addressing the nuances of generating Verilog code for each category. Empirical evidence from experiments highlights notable improvements in terms of the percentage of generated Verilog outputs that are syntactically and functionally correct. These findings underscore the efficacy of our approach, promising a forward leap in the field of automated hardware design through machine learning.

Related papers

• Studying and Benchmarking Large Language Models For Log Level Suggestion [49.176736212364496]
  Large Language Models (LLMs) have become a focal point of research across various domains. This paper investigates the impact of characteristics and learning paradigms on the performance of 12 open-source LLMs in log level suggestion.
  arXiv  Detail & Related papers  (2024-10-11T03:52:17Z)
• Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models [54.51932175059004]
  We introduce a scalable method for generating synthetic instructions to enhance the code generation capability of Large Language Models. The proposed algorithm, Genetic-Instruct, mimics evolutionary processes, utilizing self-instruction to create numerous synthetic samples from a limited number of seeds.
  arXiv  Detail & Related papers  (2024-07-29T20:42:59Z)
• CodeV: Empowering LLMs for Verilog Generation through Multi-Level Summarization [37.4446786461791]
  This paper introduces CodeV, a series of open-source instruction-tuned Verilog generation LLMs. We show that CodeV relatively surpasses the previous open-source SOTA by 14.4% (BetterV in VerilogEval) and 11.3% (RTLCoder in RTLLM) respectively.
  arXiv  Detail & Related papers  (2024-07-15T03:57:20Z)
• AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data [64.69872638349922]
  We present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. We propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review.
  arXiv  Detail & Related papers  (2024-05-29T16:57:33Z)
• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [56.019447113206006]
  Large Language Models (LLMs) have achieved remarkable progress in code generation. CodeIP is a novel multi-bit watermarking technique that embeds additional information to preserve provenance details. Experiments conducted on a real-world dataset across five programming languages demonstrate the effectiveness of CodeIP.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework [50.02710905062184]
  This paper proposes an automated design-data augmentation framework, which generates high-volume and high-quality natural language aligned with Verilog and EDA scripts. The accuracy of Verilog generation surpasses that of the current state-of-the-art open-source Verilog generation model, increasing from 58.8% to 70.6% with the same benchmark.
  arXiv  Detail & Related papers  (2024-03-17T13:01:03Z)
• BetterV: Controlled Verilog Generation with Discriminative Guidance [11.162807308782751]
  We propose a Verilog generation framework, BetterV, which fine-tunes the large language models (LLMs) on processed domain-specific runtime. BetterV has the ability to generate syntactically and functionally correct Verilog, which can outperform GPT-4 on the VerilogEval benchmark.
  arXiv  Detail & Related papers  (2024-02-03T08:00:12Z)
• Exploring Large Language Models for Code Explanation [3.2570216147409514]
  Large Language Models (LLMs) have made remarkable strides in Natural Language Processing. This study specifically delves into the task of generating natural-language summaries for code snippets, using various LLMs.
  arXiv  Detail & Related papers  (2023-10-25T14:38:40Z)
• VerilogEval: Evaluating Large Language Models for Verilog Code Generation [6.88526119890374]
  We present a comprehensive evaluation dataset consisting of 156 problems from the Verilog instructional website HDLBits. The evaluation set consists of a diverse set of Verilog code generation tasks, ranging from simple combinational circuits to complex finite state machines.
  arXiv  Detail & Related papers  (2023-09-14T09:15:34Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)
• Benchmarking Large Language Models for Automated Verilog RTL Code Generation [21.747037230069854]
  We characterize the ability of large language models (LLMs) to generate useful Verilog. We construct an evaluation framework comprising test-benches for functional analysis and a flow to test the syntax of Verilog code. Our findings show that across our problem scenarios, the fine-tuning results in LLMs more capable of producing syntactically correct code.
  arXiv  Detail & Related papers  (2022-12-13T16:34:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.