# Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring

arXiv: https://arxiv.org/abs/2203.04496 · HTML: https://mlsanity.com/paper/2203.04496/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2022.

## Built on by (most cited first, 4 of 4)

- [NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras](https://mlsanity.com/paper/2207.12496/) (2022)
- [MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices](https://mlsanity.com/paper/2411.19442/) (2024)
- [SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT](https://mlsanity.com/paper/2207.08818/) (2022)
- [Ternary-Input Binary-Weight CNN Accelerator Design for Miniature Object Classification System with Query-Driven Spatial DVS](https://mlsanity.com/paper/2512.00138/) (2025)

## Abstract (arXiv)

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.