Neuromorphic Energy Efficient Stress Detection System using Spiking Neural Network

Reliable, low-power stress-detection ‘at the edge’ is important in the context of monitoring post-stroke patients. In this context, we have developed feed-forward spiking neural networks (SNNs) for stress-detection using physiological timeseries signals of electrodermal activity (EDA), body temperature, and a multi-modal signal comprised of both. Execution of the SNNs on Intel Loihi-2 (a neuromorphic research chip) showed 5× to 83×, and 9× to 123× better energy-delay product (EDP) compared to equivalent ANNs executed on a low-power edgeGPU, and FPGA respectively. We report that the largest EDP gains (83×) are obtained for the multi-modal SNN, which has „9× less number of parameters and is „9× faster in inference latency than a conventional ANN executed on an edge-GPU.