Andrea Bejarano-Carbo, Hyochan An, Kyojin Choo, Shiyu Liu, Dennis Sylvester,
David Blaauw and Hun-Seok Kim,
Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring
inyML Research Symposium’22, March 2022, San Jose, CA
rXiv:2203.04496v1 [eess.SP] 9 Mar 2022
University of Michigan, Ann Arbor, Michigan, USA
ABSTRACT 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 6.7×7×5mm 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 nonidealities caused by a mm-scale lens and ULP front-end. The system can store 74 frames or offload data wirelessly, consuming 49.6μW on average for an expected battery lifetime of 7 days.
Fig: Imager system cross-section
Acknowledgments:The authors would like to thank Sony Semiconductor Solutions Corp./Sony electronics Inc. for supporting this work.
