Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Che-Ming Chang, Prashanth Vijayaraghavan, Ashutosh Jadhav, Charles Mackin, Vandana Mukherjee, Hsinyu Tsai, Ehsan Degan

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.

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

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.
Paper Structure (36 sections, 2 equations, 4 figures, 6 tables, 1 algorithm)

This paper contains 36 sections, 2 equations, 4 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. RTLOpt: HDL Optimization Dataset
  3. Methodology
  4. Dialectic Reasoning Agents
  5. Articulator
  6. Hypothesis Partner
  7. Executor Agents
  8. Domain-Specific Coding Agent (DCA)
  9. Code Evaluation Agent (CEA)
  10. Integrated Optimization Workflow
  11. Experiments
  12. Baselines
  13. Metrics
  14. Results
  15. (RQ1) Effectiveness of CodMas
  16. ...and 21 more sections

Figures (4)

  • Figure 1: Overview of the CodMas framework, illustrating dialectic interaction between agents and iterative refinement of RTL code.
  • Figure 2: Illustration of the complete CodMas architecture, showing dialectic agents (Articulator & Hypothesis Partner), the executor agents (Domain-Specific Coding Agent & Code Evaluation Agent), and the iterative RTL refinement loop (e.g., pipelined array multiplier). The example highlights a pipelined array multiplier optimization, with feedback from different components guiding successive refinements toward improved PPA metrics.
  • Figure 3: Ablation comparing CODMAS with a shared-memory multi-agent (SMA) variant where the Articulator and Hypothesis Partner roles are combined.
  • Figure 4: Impact of iterative refinement in CodMas on pipelining timing improvement (%) for easy (left) and hard (right) RTLOpt problems. Early iterations yield substantial gains; later iterations show diminishing or unstable returns, especially for harder cases.