Source-linked AI summary
A Multi-Expert Large Language Model Architecture for Verilog Code Generation
Bardia Nadimi, Hao Zheng
TL;DR
Earlier Verilog-generation studies used a single expert across design types and complexity levels. MEV-LLM uses complexity-specific experts and a categorized dataset; the authors report up to 23.9% improvement in pass@k over prior approaches.
Problem
Earlier studies fine-tuned a single expert on the whole dataset, limiting its capacity to distinguish design complexity levels across varied Verilog styles.
Source: A. Multi-Expert Verilog LLM (MEV-LLM) · p. 2Method
MEV-LLM uses multiple LLM experts fine-tuned for categories of similar design complexity, alongside a categorized dataset with descriptions for each entry.
Source: §I · pp. 1–2Results
Up to 23.9% improvement in pass@k over state-of-the-art approaches, including CodeGen-Verilog and GEMMA.
Source: §I · p. 2Takeaways & Limitations
The authors conclude that MEV-LLM realizes its anticipated benefits and propose more varied datasets and further study of design complexity levels.
Source: §V · p. 4
Abstract
from arXiv · showhide
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.
I. INTRODUCTION AND MOTIVATION
The introduction identifies limited labeled data, few fine-tuning methods, and syntax and functionality errors as challenges for LLM-based Verilog generation. It proposes complexity-specialized experts and a categorized dataset, reporting gains of up to 23.9% in pass@k.
I. INTRODUCTION AND MOTIVATION
- Prior Verilog-generation work is described as limited by scarce labeled data, few innovative fine-tuning methods, and frequent syntax and functionality errors.The section also notes that hardware code synthesis has seen relatively less exploration than software program synthesis.Source: §I · p. 1
- MEV-LLM assigns experts to design categories of similar complexity, with each expert fine-tuned for its category’s requirements.The architecture is introduced as a multi-expert strategy based on design complexity implied by the input prompt.Source: §I · p. 1
- The paper develops a dataset categorized by design complexity and enriched with descriptive details for fine-tuning.The introduction presents the dataset as a contribution alongside the architecture.Source: §I · pp. 1–2
- Up to 23.9% improvement in pass@k is reported for Verilog generation over state-of-the-art approaches including CodeGen-Verilog and GEMMA.The introduction names CodeGen-Verilog, [19] and GEMMA as comparison approaches.Source: §I · p. 2
II. RELATED WORKS
The related-work section surveys LLM applications to Verilog and other hardware-design tasks, including code generation, benchmarking, and design workflows. It notes that the discussed studies face challenges producing syntactically and functionally accurate Verilog.
II. RELATED WORKS
- VerilogEval benchmarks generation across tasks, while prior fine-tuning work reports syntactic accuracy and identifies functional correctness as an area for improvement.The section describes VerilogEval as using 156 HDLBits problems and ranging from basic circuits to finite-state machines.Source: §II · p. 2
- ChipGPT generates and refines Verilog from prompts, while RTLLM benchmarks RTL creation and presents self-planning prompt engineering.The section describes RTLLM as evaluating syntax, functionality, and design quality.Source: §II · p. 2
- Chip-Chat [10] describes an engineer and an LLM co-designing an 8-bit microprocessor, resulting in an AI-generated HDL design for tape-out.Source: §II · p. 2
- The section characterizes the previously discussed studies as facing challenges in producing Verilog that is both syntactically and functionally accurate.Source: §II · p. 2
III. METHODOLOGY AND DATASET
The section is introduced as covering the multi-expert LLM architecture and the process for constructing the categorized dataset.
III. METHODOLOGY AND DATASET
- The section outlines the multi-expert architecture first, followed by the categorized-dataset process.Source: §III · p. 2
A. Multi-Expert Verilog LLM (MEV-LLM)
The section contrasts single-expert fine-tuning with MEV-LLM’s complexity-specialized approach. A classifier selects among four expert models whose weights are tailored to different design-complexity categories.
A. Multi-Expert Verilog LLM (MEV-LLM)
- A single expert fine-tuned on the full dataset is described as restricted in distinguishing design complexity and adapting to varied coding styles.The section notes that Verilog designs can be combinational or sequential and that coding strategy may vary with complexity.Source: A. Multi-Expert Verilog LLM (MEV-LLM) · p. 2
- MEV-LLM uses a complexity classifier trained on design descriptions and categories to select the expert for an input prompt.The classifier assesses the prompted design’s complexity before expert selection.Source: A. Multi-Expert Verilog LLM (MEV-LLM) · pp. 2–3
- Four expert models use separate fine-tuned weights for four dataset categories, generating Verilog tailored to the classified complexity tier.The appropriate weight set is loaded after the classifier identifies the design’s complexity level.Source: A. Multi-Expert Verilog LLM (MEV-LLM) · p. 3
B. Dataset Development Method
The dataset is built from public Verilog files with generated descriptions and labels, then organized into four design-complexity categories. The authors use these annotations to support fine-tuning and report 31,104 entries with both fine- and coarse-grained labels.
B. Dataset Development Method
- Fine-grained labeling pairs generated descriptions with Verilog files, while coarse-grained labeling assigns each entry to a complexity category.The authors describe dataset categorization using chatGPT-3.5-Turbo and the generated descriptions, complexity definitions, and code.Source: B. Dataset Development Method · p. 3
- The dataset distinguishes Basic, Intermediate, Advanced, and Expert designs, ranging from wiring and gates to composite designs.Advanced examples include sequential circuits and finite-state machines; Intermediate examples include multiplexers, adders, and arithmetic units.Source: B. Dataset Development Method · p. 3
- 31,104 GitHub-derived entries carry fine- and coarse-grained labels, with descriptions generated from README files and code using chatGPT-3.5-Turbo.The description-generation prompts had a 4096-token limit.Source: B. Dataset Development Method · p. 3
A. Baseline LLMs and fine-tuning
The study fine-tunes CodeGen and GEMMA models, including complexity-specific MEV experts and a single-model supervised baseline. The MEV setup uses coarse-grained labels to select category data and fine-grained labels as description-code pairs.
A. Baseline LLMs and fine-tuning
- The study uses CodeGen 2B, 6B, and 16B, plus GEMMA 2B and 7B, as the pretrained model families.The CodeGen 16B model required three Nvidia A100 and two Nvidia A40 GPUs; the other listed models used two A100 GPUs.Source: A. Baseline LLMs and fine-tuning · p. 3
1) Fine-tuning MEV:
- For MEV fine-tuning, each baseline gets four category-specific experts, while a separate model is trained to classify design complexity.Coarse-grained labels filter entries by category, and fine-grained labels provide description-code pairs for expert fine-tuning.Source: 1) Fine-tuning MEV · p. 3
2) Supervised Fine-tuning:
- The supervised fine-tuning baseline trains a single expert on description-code pairs spanning all dataset categories without category division.Source: 2) Supervised Fine-tuning · p. 3
B. Evaluation, Comparison, and Explanation of the Results
The reported MEV-LLM improvement reaches 23.9% in pass@k over prior studies, while the authors also report a gain from fine-grained labels. Evaluation compares models using shared prompts and pass@1, pass@5, and pass@10.
B. Evaluation, Comparison, and Explanation of the Results
- Each model receives identical prompts and produces 15 results per problem for pass@1, pass@5, and pass@10 evaluation.The benchmark includes Verilog-Human problems with 156 items and Verilog-Machine problems with 143 items; pass@k counts a problem as solved when at least one of k samples passes unit tests.Source: B. Evaluation, Comparison, and… · p. 4
- The authors report that supervised fine-tuning improves code generation over unsupervised fine-tuning and that task-tailored multi-expert models improve generation quality.These conclusions are drawn from the results in Tables II and III.Source: B. Evaluation, Comparison, and… · p. 4
- Up to 23.9% improvement in pass@k is reported for MEV-LLM over previous studies by Thakur et al..The comparison tables cover CodeGen-Verilog and GEMMA models against proposed models.Source: B. Evaluation, Comparison, and… · p. 4, Table II · p. 4, Table III · p. 4
- Up to 9.8% improvement in pass@k is attributed to fine-grained labels versus prior research, with the LLM architecture held constant.The authors identify the training dataset as the distinction between the compared instances.Source: B. Evaluation, Comparison, and… · p. 4
C. Dataset Quality
The dataset-quality assessment deliberately mismatches code and descriptions, then compares fine-tuning results against an accurate dataset. The authors report that erroneous data significantly affects generation and emphasize dataset quality for fine-tuning.
C. Dataset Quality
- The authors randomly shuffle codes and descriptions to create mismatched entries, then fine-tune GEMMA-2B using the flawed and accurate datasets.Source: C. Dataset Quality · p. 4
- Generation capabilities are significantly affected by the erroneous dataset, which the authors use to support the importance of dataset quality in fine-tuning.Source: C. Dataset Quality · p. 4
- Because descriptions generated via chatGPT-3.5-Turbo could not all be verified, the authors follow prior methodology to check generated labels.Source: C. Dataset Quality · p. 4
V. CONCLUSION AND FUTURE WORKS
The conclusion presents MEV-LLM with a categorized, description-enriched dataset and reports an improvement of up to 23.9% in pass@k. It identifies broader datasets and further study of design complexity levels as potential avenues for advancement.
V. CONCLUSION AND FUTURE WORKS
- Up to 23.9% improvement in the pass@k metric was recorded for MEV-LLM.The conclusion also identifies more varied, comprehensive datasets and further investigation of complexity levels as potential avenues for advancement.Source: §V · p. 4
- MEV-LLM is introduced alongside a categorized dataset enriched with descriptions for each entry.The architecture aims to improve results across Verilog varieties and complexity tiers.Source: §V · p. 4
- The authors identify compiling more varied, comprehensive datasets as a potential route to further advancement.Source: §V · p. 4
- Further investigation into different complexity levels is also identified as a potential avenue for advancement.Source: §V · p. 4