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Natural language is not enough: Benchmarking multi-modal generative AI for Verilog generation

Kaiyan Chang, Zhirong Chen, Yunhao Zhou, Wenlong Zhu, kun wang, Haobo Xu, Cangyuan Li, Mengdi Wang, Shengwen Liang, Huawei Li, Yinhe Han, Ying Wang

TL;DR

This paper shows that natural language alone is insufficient for Verilog generation in spatially complex hardware and proposes an open-source multi-modal benchmark plus a Verilog Large Model Query Language (VLMQL) for vision-language co-design. It formalizes a benchmark framework with hierarchical difficulty, multi-level prompting, and fine-grained token metrics to evaluate multi-modal models, and demonstrates significant improvements in syntax and functional correctness over NL-only baselines using GPT-4V and LLaMA variants. The work provides practical tooling and datasets to drive progress in hardware design with large multimodal models, suggesting that visual context can substantially reduce misalignment and improve design fidelity. Overall, it enables a more scalable and diversified approach to hardware design in the era of large hardware-design models by standardizing evaluation and facilitating efficient multi-modal generation workflows.

Abstract

Natural language interfaces have exhibited considerable potential in the automation of Verilog generation derived from high-level specifications through the utilization of large language models, garnering significant attention. Nevertheless, this paper elucidates that visual representations contribute essential contextual information critical to design intent for hardware architectures possessing spatial complexity, potentially surpassing the efficacy of natural-language-only inputs. Expanding upon this premise, our paper introduces an open-source benchmark for multi-modal generative models tailored for Verilog synthesis from visual-linguistic inputs, addressing both singular and complex modules. Additionally, we introduce an open-source visual and natural language Verilog query language framework to facilitate efficient and user-friendly multi-modal queries. To evaluate the performance of the proposed multi-modal hardware generative AI in Verilog generation tasks, we compare it with a popular method that relies solely on natural language. Our results demonstrate a significant accuracy improvement in the multi-modal generated Verilog compared to queries based solely on natural language. We hope to reveal a new approach to hardware design in the large-hardware-design-model era, thereby fostering a more diversified and productive approach to hardware design.

Natural language is not enough: Benchmarking multi-modal generative AI for Verilog generation

TL;DR

This paper shows that natural language alone is insufficient for Verilog generation in spatially complex hardware and proposes an open-source multi-modal benchmark plus a Verilog Large Model Query Language (VLMQL) for vision-language co-design. It formalizes a benchmark framework with hierarchical difficulty, multi-level prompting, and fine-grained token metrics to evaluate multi-modal models, and demonstrates significant improvements in syntax and functional correctness over NL-only baselines using GPT-4V and LLaMA variants. The work provides practical tooling and datasets to drive progress in hardware design with large multimodal models, suggesting that visual context can substantially reduce misalignment and improve design fidelity. Overall, it enables a more scalable and diversified approach to hardware design in the era of large hardware-design models by standardizing evaluation and facilitating efficient multi-modal generation workflows.

Abstract

Natural language interfaces have exhibited considerable potential in the automation of Verilog generation derived from high-level specifications through the utilization of large language models, garnering significant attention. Nevertheless, this paper elucidates that visual representations contribute essential contextual information critical to design intent for hardware architectures possessing spatial complexity, potentially surpassing the efficacy of natural-language-only inputs. Expanding upon this premise, our paper introduces an open-source benchmark for multi-modal generative models tailored for Verilog synthesis from visual-linguistic inputs, addressing both singular and complex modules. Additionally, we introduce an open-source visual and natural language Verilog query language framework to facilitate efficient and user-friendly multi-modal queries. To evaluate the performance of the proposed multi-modal hardware generative AI in Verilog generation tasks, we compare it with a popular method that relies solely on natural language. Our results demonstrate a significant accuracy improvement in the multi-modal generated Verilog compared to queries based solely on natural language. We hope to reveal a new approach to hardware design in the large-hardware-design-model era, thereby fostering a more diversified and productive approach to hardware design.
Paper Structure (41 sections, 2 equations, 7 figures, 6 tables)

This paper contains 41 sections, 2 equations, 7 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Background & Motivation
  3. The limitations in current LLM-based RTL generation roadmap.
  4. Limitation 1: The inherent limitations of linguistic representations render natural language insufficient for accurately conveying the nested spatial structures of hardware.
  5. Limitation 2: Inefficiency of Linguistic Descriptions in Multi-Module Hardware Design
  6. Limitation 3: Risk of Misalignment in Complex Hardware Designs Using Language Models
  7. Visual and Natural Language Hardware Co-Design Case Study
  8. Multi-Module Hardware Generation
  9. State Machine Generation
  10. Motivation: Do we need a new benchmark?
  11. Challenge I: Unstandardized Modal Input
  12. Challenge II: Lack of Complexity Categorization for Multi-Modal Input Prompts
  13. Challenge III: Coarse-grain output metrics
  14. Visual and Natural Language Hardware Co-Design Workflow
  15. Visual and Natural Language Co-Generation Workflow
  16. ...and 26 more sections

Figures (7)

  • Figure 1: Constraints in Hardware Design Using Natural Language: An Analysis from Three Perspectives. The co-design row illustrates solutions leveraging the multi-modal approach. The "natural language only" row represents results derived from a text-only language model. The green sentence indicates redundant information that can be more efficiently conveyed through visual representation.
  • Figure 2: A case study to show a multiply and accumulate PE.
  • Figure 3: A comprehensive case study illustrating that, within the context of multi-module hardware, the multi-modal model exhibits a substantial enhancement in performance compared to the conventional language model.
  • Figure 4: A comprehensive analysis of a state machine elucidating the superiority of integrated visual and natural language co-design over conventional language models. This figure illustrates a state machine designed to identify the input sequence 10011.
  • Figure 5: Compare the capability of generating Verilog between multi-modal and natural-language-only model. To follow a fair standard, we generate a Verilog description and test the generated code using pass@5.
  • ...and 2 more figures