CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization

2026-03-17

AI Summary

This work addresses the challenge of power, performance, and area (PPA) optimization for register-transfer level (RTL) code by introducing the first multi-agent collaborative framework that integrates structured dialectical reasoning. The approach employs two synergistic agents—an Articulator and a Hypothesis Partner—to uncover implicit assumptions and correct optimization biases, thereby guiding a domain-aware coding agent to produce architecture-sensitive Verilog modifications. A deterministic evaluation agent then validates these changes for syntactic correctness, functional equivalence, and PPA improvements. Evaluated on the RTLOPT benchmark, the method achieves approximately 25% reduction in critical-path delay through pipelining and 22% power savings via clock gating, while substantially decreasing functional and compilation errors, outperforming strong prompt-engineering baselines and existing agent-based approaches.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Optimizing Register Transfer Level (RTL) code is a critical step in Electronic Design Automation (EDA) for improving power, performance, and area (PPA). We present CODMAS (Collaborative Optimization via a Dialectic Multi-Agent System), a framework that combines structured dialectic reasoning with domain-aware code generation and deterministic evaluation to automate RTL optimization. At the core of CODMAS are two dialectic agents: the Articulator, inspired by rubber-duck debugging, which articulates stepwise transformation plans and exposes latent assumptions; and the Hypothesis Partner, which predicts outcomes and reconciles deviations between expected and actual behavior to guide targeted refinements. These agents direct a Domain-Specific Coding Agent (DCA) to generate architecture-aware Verilog edits and a Code Evaluation Agent (CEA) to verify syntax, functionality, and PPA metrics. We introduce RTLOPT, a benchmark of 120 Verilog triples (unoptimized, optimized, testbench) for pipelining and clock-gating transformations. Across proprietary and open LLMs, CODMAS achieves ~25% reduction in critical path delay for pipelining and ~22% power reduction for clock gating, while reducing functional and compilation failures compared to strong prompting and agentic baselines. These results demonstrate that structured multi-agent reasoning can significantly enhance automated RTL optimization and scale to more complex designs and broader optimization tasks.

Problem

• RTL optimization
• Electronic Design Automation
• Power-Performance-Area
• Register Transfer Level
• Automated Code Optimization

Innovation

• dialectic multi-agent system
• RTL optimization
• structured reasoning
• domain-aware code generation
• RTLOPT benchmark