VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation
The increasing computational demands in the field of electronic design automation have led to a growing interest in the automation of Register Transfer Level (RTL) design. Recent advancements in Large Language Models (LLMs) offer a promising avenue for Hardware Description Language (HDL) generation; however, these models face significant challenges, primarily due to their limited parametric knowledge and adherence to domain-specific constraints.
While techniques such as prompt engineering and fine-tuning have been employed to improve LLM performance in this area, they are often constrained by knowledge coverage and incur high training costs. In response to these challenges, multi-agent architectures have emerged as a viable alternative, offering a training-free paradigm that enhances reasoning through collaborative generation. Nonetheless, existing multi-agent approaches are hindered by two critical deficiencies: susceptibility to noise propagation and limited exploration of the reasoning space.
Introducing VeriMoA
To address these shortcomings, we introduce VeriMoA, a novel mixture-of-agents (MoA) framework designed specifically for specification-to-HDL (spec-to-HDL) generation. The framework incorporates two synergistic innovations aimed at improving the overall quality and efficiency of the generation process.
- Quality-Guided Caching Mechanism: This innovative feature maintains all intermediate HDL outputs during the generation process. By enabling quality-based ranking and selection, it encourages knowledge accumulation across layers of reasoning. This approach not only enhances the final output quality but also minimizes the risk of noise propagation throughout the generation process.
- Multi-Path Generation Strategy: VeriMoA employs a multi-path generation strategy that utilizes C++ and Python as intermediate representations. This decomposition of spec-to-HDL translation into two distinct stages allows the framework to leverage the fluency of LLMs in high-resource programming languages. It also promotes solution diversity, leading to more robust HDL generation.
Experimental Results
Comprehensive experiments conducted using the VerilogEval 2.0 and RTLLM 2.0 benchmarks have demonstrated the effectiveness of VeriMoA. The results indicate that the framework achieves significant improvements in performance, specifically a 15-30% increase in the Pass@1 metric across a variety of LLM backbones. Notably, this improvement allows smaller models to perform comparably to their larger counterparts and fine-tuned alternatives, all without incurring the costs associated with extensive training.
The implications of these findings are substantial, indicating that VeriMoA not only enhances the efficiency of HDL generation but also democratizes access to high-quality design automation tools. As developers strive to meet rising computational demands, solutions like VeriMoA could play a pivotal role in shaping the future of RTL design.