Year: 2025
Type: article
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 article 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 28-nm prototype SteROI-D design achieves up to$3.92\times $reduction in total system energy compared with a baseline application specific integrated circuit (ASIC).
Authors: Jack Erhardt, Ziang Li, Reid Pinkham, Andrew Berkovich, Zhengya Zhang
Institution: University of Michigan
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083292800