CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908896494354432 |
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| author | Chang, Che-Ming Vijayaraghavan, Prashanth Jadhav, Ashutosh Mackin, Charles Mukherjee, Vandana Tsai, Hsinyu Degan, Ehsan |
| author_facet | Chang, Che-Ming Vijayaraghavan, Prashanth Jadhav, Ashutosh Mackin, Charles Mukherjee, Vandana Tsai, Hsinyu Degan, Ehsan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_17204 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization Chang, Che-Ming Vijayaraghavan, Prashanth Jadhav, Ashutosh Mackin, Charles Mukherjee, Vandana Tsai, Hsinyu Degan, Ehsan Computation and Language Hardware Architecture Programming Languages 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. |
| title | CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization |
| topic | Computation and Language Hardware Architecture Programming Languages |
| url | https://arxiv.org/abs/2603.17204 |