  Millimeter-scale embedded sensing systems have unique advantages over larger
devices as they are able to capture, analyze, store, and transmit data at the
source while being unobtrusive and covert. However, area-constrained systems
pose several challenges, including a tight energy budget and peak power,
limited data storage, costly wireless communication, and physical integration
at a miniature scale. This paper proposes a novel 6.7$\times$7$\times$5mm
imaging system with deep-learning and image processing capabilities for
intelligent edge applications, and is demonstrated in a home-surveillance
scenario. The system is implemented by vertically stacking custom
ultra-low-power (ULP) ICs and uses techniques such as dynamic behavior-specific
power management, hierarchical event detection, and a combination of data
compression methods. It demonstrates a new image-correcting neural network that
compensates for non-idealities caused by a mm-scale lens and ULP front-end. The
system can store 74 frames or offload data wirelessly, consuming 49.6$\mu$W on
average for an expected battery lifetime of 7 days.
