SteROI-D: System Design and Mapping for Stereo Depth Inference on Regions of Interest
Abstract
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.
Keywords
Cite
@article{arxiv.2502.09528,
title = {SteROI-D: System Design and Mapping for Stereo Depth Inference on Regions of Interest},
author = {Jack Erhardt and Ziang Li and Reid Pinkham and Andrew Berkovich and Zhengya Zhang},
journal= {arXiv preprint arXiv:2502.09528},
year = {2025}
}
Comments
Accepted as a full paper by the 2025 EDGE AI FOUNDATION Austin