通过关注重点区域降低深度计算能耗，提升AR/VR设备续航
SteROI-D: System Design and Mapping for Stereo Depth Inference on Regions of Interest
- 系统级设计聚焦兴趣区域与时间稀疏性，动态分配算力
- 28nm芯片实测能效比基线ASIC提升4.35倍
- 适合资源受限的AR/VR头显等移动设备使用
机器学习算法已使高质量立体深度估计可在增强现实和虚拟现实（AR/VR）设备上运行。然而，全图像处理栈的高功耗阻碍了立体深度算法在电池供电设备上的有效部署。本文提出SteROI-D，一个完整的立体深度系统及其配套映射方法。该系统在系统层面利用兴趣区域（ROI）与时间稀疏性实现节能。其灵活异构的计算架构支持多种兴趣区域。尤为重要的是，我们引入了一种系统性映射方法，以高效处理动态兴趣区域，从而最大化节能效果。采用这些技术，我们的28nm原型设计相比基线ASIC实现高达4.35倍的系统总能耗降低。
原文摘要 · Abstract (English)
Machine learning algorithms have enabled high quality stereo depth estimation to run on Augmented and Virtual Reality (AR/VR) devices. However, high energy consumption across the full image processing stack prevents stereo depth algorithms from running effectively on battery-limited devices. This paper introduces SteROI-D, a full stereo depth system paired with a mapping methodology. SteROI-D exploits Region-of-Interest (ROI) and temporal sparsity at the system level to save energy. SteROI-D's flexible and heterogeneous compute fabric supports diverse ROIs. Importantly, we introduce a systematic mapping methodology to effectively handle dynamic ROIs, thereby maximizing energy savings. Using these techniques, our 28nm prototype SteROI-D design achieves up to 4.35x reduction in total system energy compared to a baseline ASIC.
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