用扩散模型优化加法器乘法器，生成更高效电路设计
AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models
- 将电路设计转化为条件图像生成任务，利用扩散模型生成高质量电路
- 在多个指标上实现更好权衡，优于现有最优方法
- 适合从事数字电路优化与AI辅助设计的研究者
算术电路（如加法器和乘法器）是数字系统的核心组件，直接影响性能、功耗和面积。然而，由于设计空间巨大且受物理约束复杂，优化仍具挑战性。尽管基于深度学习的方法已展现出潜力，但难以持续探索高价值设计变体，限制了优化效率。为此，我们提出AC-Refiner，一种基于条件扩散模型的算术电路优化框架。核心思路是将电路综合重构为条件图像生成任务，通过精准控制去噪过程以目标质量指标（QoR）为条件，持续生成优质电路设计。同时，生成的设计用于微调扩散模型，聚焦于帕累托前沿区域。实验表明，AC-Refiner生成的设计在帕累托最优性上显著优于现有先进基线方法，其性能提升也在实际应用中得到验证。
原文摘要 · Abstract (English)
Arithmetic circuits, such as adders and multipliers, are fundamental components of digital systems, directly impacting the performance, power efficiency, and area footprint. However, optimizing these circuits remains challenging due to the vast design space and complex physical constraints. While recent deep learning-based approaches have shown promise, they struggle to consistently explore high-potential design variants, limiting their optimization efficiency. To address this challenge, we propose AC-Refiner, a novel arithmetic circuit optimization framework leveraging conditional diffusion models. Our key insight is to reframe arithmetic circuit synthesis as a conditional image generation task. By carefully conditioning the denoising diffusion process on target quality-of-results (QoRs), AC-Refiner consistently produces high-quality circuit designs. Furthermore, the explored designs are used to fine-tune the diffusion model, which focuses the exploration near the Pareto frontier. Experimental results demonstrate that AC-Refiner generates designs with superior Pareto optimality, outperforming state-of-the-art baselines. The performance gain is further validated by integrating AC-Refiner into practical applications.
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