SunSift: Solar-Powered Intelligent Sensing through Informative Sample Selection

Abstract

SunSift is a lightweight framework for batteryless CNN inference on energy-harvesting IoT devices. It uses intelligent voltage-based state transitions and selective MRAM checkpointing to support intermittent execution under frequent power failures. The system is implemented using Arduino Nano 33 BLE boards and TensorFlow Lite for Microcontrollers, achieving 71.51% accuracy in identifying informative samples from CIFAR-10 for distributed federated learning. This work demonstrates a practical path toward sustainable, batteryless edge intelligence.