SteROI-D: System Design and Mapping for Stereo Depth Inference on Regions of Interest

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.

@article{erhardt2025_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 }
}