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 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.92\times $</tex-math> </inline-formula> reduction in total system energy compared with a baseline application specific integrated circuit (ASIC).