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RTLSquad: Multi-Agent Based Interpretable RTL Design

Bowei Wang, Qi Xiong, Zeqing Xiang, Lei Wang, Renzhi Chen

TL;DR

RTLSquad introduces an LLM-based multi-agent system for RTL design that prioritizes decision interpretability alongside functional correctness and PPA optimization. By dividing the workflow into exploration, implementation, and verification & evaluation stages and organizing agents into specialized squads, the framework generates RTL code with explicit decision paths documented during inter-agent communication. Empirical results show RTLSquad enhances Pass@1 scores by about $7.2\%$ and often matches or exceeds reference PPA in a majority of cases (about $73.3\%$), while providing structured explanations for each design decision. This approach enables hardware engineers to trust and auditable the RTL generation process, facilitating integration into existing design flows and potentially accelerating RTL optimization tasks.

Abstract

Optimizing Register-Transfer Level (RTL) code is crucial for improving hardware PPA performance. Large Language Models (LLMs) offer new approaches for automatic RTL code generation and optimization. However, existing methods often lack decision interpretability (sufficient, understandable justification for decisions), making it difficult for hardware engineers to trust the generated results, thus preventing these methods from being integrated into the design process. To address this, we propose RTLSquad, a novel LLM-Based Multi-Agent system for interpretable RTL code generation. RTLSquad divides the design process into exploration, implementation, and verification & evaluation stages managed by specialized agent squads, generating optimized RTL code through inter-agent collaboration, and providing decision interpretability through the communication process. Experiments show that RTLSquad excels in generating functionally correct RTL code and optimizing PPA performance, while also having the capability to provide decision paths, demonstrating the practical value of our system.

RTLSquad: Multi-Agent Based Interpretable RTL Design

TL;DR

RTLSquad introduces an LLM-based multi-agent system for RTL design that prioritizes decision interpretability alongside functional correctness and PPA optimization. By dividing the workflow into exploration, implementation, and verification & evaluation stages and organizing agents into specialized squads, the framework generates RTL code with explicit decision paths documented during inter-agent communication. Empirical results show RTLSquad enhances Pass@1 scores by about and often matches or exceeds reference PPA in a majority of cases (about ), while providing structured explanations for each design decision. This approach enables hardware engineers to trust and auditable the RTL generation process, facilitating integration into existing design flows and potentially accelerating RTL optimization tasks.

Abstract

Optimizing Register-Transfer Level (RTL) code is crucial for improving hardware PPA performance. Large Language Models (LLMs) offer new approaches for automatic RTL code generation and optimization. However, existing methods often lack decision interpretability (sufficient, understandable justification for decisions), making it difficult for hardware engineers to trust the generated results, thus preventing these methods from being integrated into the design process. To address this, we propose RTLSquad, a novel LLM-Based Multi-Agent system for interpretable RTL code generation. RTLSquad divides the design process into exploration, implementation, and verification & evaluation stages managed by specialized agent squads, generating optimized RTL code through inter-agent collaboration, and providing decision interpretability through the communication process. Experiments show that RTLSquad excels in generating functionally correct RTL code and optimizing PPA performance, while also having the capability to provide decision paths, demonstrating the practical value of our system.
Paper Structure (18 sections, 1 equation, 4 figures, 2 tables)

This paper contains 18 sections, 1 equation, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLM-Based Agent for RTL Design
  4. Decision Interpretability with LLM-MA
  5. Methodology
  6. Overview
  7. Implementation Stage
  8. Verification & Evaluation Stage
  9. Exploration Stage
  10. Case Study: Decision Interpretability
  11. Implementation Interpretability
  12. Analysis Interpretability
  13. Exporation Interpretability
  14. Experiments
  15. Setup
  16. ...and 3 more sections

Figures (4)

  • Figure 1: The overview of RTLSquad. The workflow consists of three stages: exploration, implementation, and verification & evaluation. The iterative process is divided into inner loop and outer loop based on verification results.
  • Figure 2: The implementation stage of RTLSquad.
  • Figure 3: The Verification & Evaluation stage of RTLSquad.
  • Figure 4: The exploration stage of RTLSquad.