ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
A groundbreaking advancement in RTL (Register Transfer Level) code generation has emerged with the introduction of ChipMATE, a multi-agent training framework that leverages reinforcement learning. This innovative approach addresses significant shortcomings in existing API-based systems, which often misalign with industry practices and fail to capitalize on proprietary internal data.
Background
Traditional RTL code generation methods rely heavily on closed-source APIs, which are often incompatible with the air-gapped security requirements of chip vendors. These systems also assume the availability of a “golden” testbench during the generation process, a scenario that is rarely feasible in real-world applications. Consequently, many valuable internal data resources remain underutilized. Recent advancements in self-trained models have begun to tackle deployment constraints; however, they typically function as single-turn generators and overlook the essential verification process that is critical in industrial workflows.
Introducing ChipMATE
ChipMATE marks a significant leap forward in addressing these challenges by introducing a self-trained multi-agent framework designed specifically for RTL generation. This framework is inspired by real-world industrial practices, particularly the concept that correctness is achieved through cross-comparison among independently generated RTL modules and reference models.
- Dual-Agent System: ChipMATE integrates a Verilog agent alongside a Python reference-model agent, enabling them to mutually verify each other’s outputs without relying on a golden oracle.
- Backtrack-Based Inference Workflow: To mitigate the risk of error propagation across different turns of generation, ChipMATE employs a sophisticated backtrack-based inference workflow.
- Two-Stage Training Pipeline: The training process is structured in two stages: initially, each agent is trained individually to maximize its code-generation capabilities, followed by joint training to enhance collaborative performance.
Training and Data Generation
To facilitate effective training, ChipMATE incorporates a hybrid data-generation framework that produces 64.4K high-quality reference model training samples. This extensive dataset plays a crucial role in ensuring that the agents are well-equipped to generate robust RTL code.
Performance Metrics
ChipMATE has demonstrated impressive performance metrics, achieving pass rates of 75.0% and 80.1% on the VerilogEval V2 benchmark with base models of 4B and 9B parameters, respectively. These results surpass those of existing self-trained models and even outperform the renowned DeepSeek V4, which possesses 1600B parameters.
Public Availability
In a move towards transparency and community collaboration, the code and model weights for ChipMATE are publicly available. Interested parties can access them at https://github.com/zhongkaiyu/ChipMATE. This open-source initiative aims to encourage further research and development in the field of RTL code generation.
Conclusion
With ChipMATE, the landscape of RTL generation is set to transform, addressing longstanding issues in code generation practices while paving the way for more efficient and effective design flows in the semiconductor industry. This innovative framework not only enhances the reliability of generated code but also ensures that critical internal resources are maximally utilized, fostering a new era of chip design automation.
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