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 non-idealities 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.
Preprint is up on arXiv: https://arxiv.org/abs/2203.04496
Conference information: https://www.tinyml.org/event/summit-2022/
Personally, I find such work quite fascinating. With recent advances in learning based approaches for computer vision, we're seeing a "race to the top" --- larger neural networks, humongous datasets, and even beefier GPUs drawing 100's of watts of power. But, on the other hand, there's also a "race to the bottom" driven by edge computing/IoT applications that are extremely resource constrained --- microwatts of power, low image resolutions, and splitting hairs over every bit, every byte of data transferred.
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