标题： AC-Refiner：使用条件扩散模型的高效算术电路优化 显示英文标题

标题： AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models

摘要： 算术电路，如加法器和乘法器，是数字系统的基本组件，直接影响性能、功耗效率和面积足迹。 然而，由于设计空间庞大和复杂的物理约束，优化这些电路仍然具有挑战性。 尽管最近基于深度学习的方法显示出潜力，但它们难以持续探索高潜力的设计变体，限制了其优化效率。 为了解决这一挑战，我们提出了AC-Refiner，一种利用条件扩散模型的新型算术电路优化框架。 我们的关键见解是将算术电路综合重新表述为一个条件图像生成任务。 通过在目标结果质量（QoRs）上仔细条件化去噪扩散过程，AC-Refiner持续生成高质量的电路设计。 此外，探索的设计用于微调扩散模型，使探索集中在帕累托前沿附近。 实验结果表明，AC-Refiner生成的设计具有优越的帕累托最优性，优于最先进的基线方法。 通过将AC-Refiner集成到实际应用中进一步验证了性能提升。 显示英文摘要

摘要： 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.