CNN-based pattern recognition on nonvolatile IoT platform for smart ultraviolet monitoring

Intelligent computing and maintenance-free powering are two desirable characteristics of wearable IoT devices. Energy harvesting nonvolatile intelligent processor (NIP) with neural network computation capability has the potential to advance these goals. Individual ultraviolet (UV) exposure monitoring progressively becomes one conspicuous application of wearable devices. In resource constrained wearable sensor nodes, we can alleviate the data transmission burden via convolutional neural networks (CNNs) based pattern recognition. Nevertheless, in spite of the substantially improved computing capability of NIP, typically computational and memory intensive CNNs are still too bulky for on-node implementation. We develop an CNN-based pattern recognition system for nonvolatile IoT platform for smart UV monitoring, and propose a optimization method to achieve extremely tiny and efficient CNNs. Experimental results show that the offline-trained CNN can recognize individual UV exposure patterns with accuracy of 85%, and the simplified on-node CNN can achieve 93.2% parameters reduction with only 5% accuracy loss.