Presentation
A Quantitative Approach to Exploring Novel Image Sensor Architectures Towards Autonomous Edge Machine Vision
DescriptionAutonomous edge machine vision requires image sensor architectures that simultaneously advance inference autonomy and energy autonomy. This paper introduces a quantitative modeling framework that spans conventional CMOS imagers and emerging designs—event-based/DVS sensors, coded-exposure sensors, and self-powered sensors with energy-harvesting pixels. We unify optical, circuit, and algorithmic modeling in a single analytical flow to capture how architectural choices in pixel structures, readout pipelines, compressive acquisition, and hybrid imaging–harvesting mechanisms propagate to power, latency, noise, and task-level vision performance. Extending first-principles models of self-powered vision systems, the framework also evaluates when energy-harvesting pixels provide system-level advantages over external solar harvesting, particularly in long-lived and hard-to-maintain deployments. By enabling rapid, physically grounded exploration and comparative analyses, our results reveal key trade-offs that determine when unconventional sensor architectures meaningfully enhance edge intelligence. This framework offers a principled foundation for designing next-generation energy-aware, autonomous machine vision systems.
Event Type
Research Special Session
TimeTuesday, July 284:30pm - 5:00pm PDT
LocationMtg Room 201A