RTLSquad: Multi-Agent Based Interpretable RTL Design

TL;DR

RTLSquad introduces a multi-agent LLM system that enhances RTL code optimization with interpretability, enabling hardware engineers to trust and understand the decision-making process in automated hardware design.

Contribution

This paper presents RTLSquad, a novel multi-agent framework that improves RTL code generation and optimization while providing decision interpretability for hardware design.

Findings

Generates functionally correct RTL code with optimized PPA.

Provides decision paths for interpretability.

Outperforms existing methods in code quality and trustworthiness.

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.