The proliferation of deep learning accelerators calls for efficient and cost-effective hardware design solutions, where parameterized modular hardware generator and electronic design automation (EDA) tools play crucial roles in improving productivity and final Quality-of-Results (QoR). To strike a good balance across multiple QoR of interest (e.g., performance, power, and area), the designers need to navigate a vast design space, encompassing tunable parameters for both hardware generator and EDA synthesis tools. However, the significant time for EDA tool invocations and complex interplay among numerous design parameters make this task extremely challenging, even for experienced designers. To address these challenges, we introduce DiffuSE, a diffusion-driven design space exploration framework for cross-layer optimization of DNN accelerators. DiffuSE leverages conditional diffusion models to capture the inverse, one-to-many mapping from QoR objectives to parameter combinations, allowing for targeted exploration within promising regions of the design space. By carefully selecting the conditioning QoR values, the framework facilitates an effective trade-off among multiple QoR metrics in a sample-efficient manner. Experimental results under 7nm technology demonstrate the superiority of the proposed framework compared to previous arts.

翻译：深度学习加速器的激增要求高效且成本效益高的硬件设计解决方案，其中参数化模块化硬件生成器和电子设计自动化（EDA）工具在提升生产力和最终结果质量方面发挥着关键作用。为了在多个关注的结果质量指标（如性能、功耗和面积）之间取得良好平衡，设计者需要探索一个庞大的设计空间，该空间涵盖硬件生成器和EDA综合工具的可调参数。然而，EDA工具调用的耗时以及众多设计参数之间复杂的相互作用，使得这项任务极具挑战性，即使对于经验丰富的设计者亦是如此。为应对这些挑战，我们提出了DiffuSE，一个用于深度神经网络加速器跨层优化的扩散驱动设计空间探索框架。DiffuSE利用条件扩散模型来捕获从结果质量目标到参数组合的逆向、一对多映射，从而允许在设计空间的有希望区域内进行针对性探索。通过精心选择条件化的结果质量值，该框架能够以样本高效的方式促进多个结果质量指标之间的有效权衡。在7纳米工艺下的实验结果表明，所提框架相较于先前技术具有优越性。