RTLSquad: Multi-Agent Based Interpretable RTL Design

2025-01-06

AI Summary

To address the lack of decision interpretability and low engineer trust in LLM-driven RTL code generation, this paper proposes the first LLM-based multi-agent system tailored for hardware design. The system decomposes the optimization workflow into three decoupled phases—exploration, implementation, and verification-assessment—enabling explicit, traceable decision-making via role-specialized agents, RTL semantic parsing, formal verification interfaces, and natural-language inter-agent communication. Innovatively integrating role-driven prompt engineering with a collaborative multi-agent architecture, it achieves 100% functional correctness across multiple benchmark circuits, improves PPA (power, performance, area) metrics by 17.3% on average, and attains 92% trust endorsement from practicing hardware engineers. This work constitutes the first systematic solution to the dual challenges of explainability and engineering credibility of LLMs in RTL design.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• RTL Code Generation
• Explainability
• Hardware Engineering

Innovation

• RTLSquad
• Collaborative Intelligent Models
• RTL Design Optimization