# A sub-mW IoT-endnode for always-on visual monitoring and smart triggering

## TL;DR

This paper introduces a sub-milliwatt IoT visual sensing node capable of always-on monitoring with smart triggering, combining a low-power binary pixel imager and FPGA-based processing for efficient, context-aware visual detection.

## Contribution

The work presents a fully-programmable, ultra-low-power visual sensing system with focal-plane processing, enabling accurate triggering at sub-mW power levels, which is a significant advancement over existing solutions.

## Key findings

- Achieves triggering accuracy comparable to RGB sensors in ambient light.
- Consumes between 193μW and 277μW depending on activity.
- Offers 19x lower power consumption than MCU-based cameras.

## Abstract

This work presents a fully-programmable Internet of Things (IoT) visual sensing node that targets sub-mW power consumption in always-on monitoring scenarios. The system features a spatial-contrast $128\mathrm{x}64$ binary pixel imager with focal-plane processing. The sensor, when working at its lowest power mode ($10\mu W$ at 10 fps), provides as output the number of changed pixels. Based on this information, a dedicated camera interface, implemented on a low-power FPGA, wakes up an ultra-low-power parallel processing unit to extract context-aware visual information. We evaluate the smart sensor on three always-on visual triggering application scenarios. Triggering accuracy comparable to RGB image sensors is achieved at nominal lighting conditions, while consuming an average power between $193\mu W$ and $277\mu W$, depending on context activity. The digital sub-system is extremely flexible, thanks to a fully-programmable digital signal processing engine, but still achieves 19x lower power consumption compared to MCU-based cameras with significantly lower on-board computing capabilities.