NeSe: Near-Sensor Event-Driven Scheme for Low Power Energy Harvesting Sensors

TL;DR

NeSe introduces a low-power, near-sensor background subtraction method for energy-harvesting visual sensors, enabling adjustable accuracy and efficient event detection with reduced data movement and resilience to power interruptions.

Contribution

The paper presents a novel background subtraction scheme using NVM for tiny energy-harvested sensors, improving accuracy, efficiency, and resiliency in event detection.

Findings

Achieves adjustable detection accuracy at runtime.

Reduces data movement overhead through near-sensor processing.

Ensures resiliency against power cuts with NVM-based background storage.

Abstract

Digital technologies have made it possible to deploy visual sensor nodes capable of detecting motion events in the coverage area cost-effectively. However, background subtraction, as a widely used approach, remains an intractable task due to its inability to achieve competitive accuracy and reduced computation cost simultaneously. In this paper, an effective background subtraction approach, namely NeSe, for tiny energy-harvested sensors is proposed leveraging non-volatile memory (NVM). Using the developed software/hardware method, the accuracy and efficiency of event detection can be adjusted at runtime by changing the precision depending on the application's needs. Due to the near-sensor implementation of background subtraction and NVM usage, the proposed design reduces the data movement overhead while ensuring intermittent resiliency. The background is stored for a specific time interval within NVMs and compared with the next frame. If the power is cut, the background remains unchanged and is updated after the interval passes. Once the moving object is detected, the device switches to the high-powered sensor mode to capture the image.