Presentation
Multi-modal Signal applied Neuromorphic proven SNN Model for Stress Detection
DescriptionReliable, and low-power stress-detector ‘at the edge' is extremely beneficial for continuous monitoring of post-stroke patients. In this context, feed-forward spiking neural networks (SNNs) for stress detection using physiological time-series signals of electrodermal activity (EDA), body temperature, and a multi-modal signal comprised of both, are designed and evaluated. 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 artificial neural networks (ANNs) executed on a low-power edge-GPU, and FPGA respectively. Significant EDP gain (83×), supplemented with fast inference rate (∼ 9×) were reported for the multi-modal SNN, which is composed of ∼ 9× less parameters in comparison with the corresponding ANN run on an edge-GPU.
Event Type
Work-in-Progress Poster
TimeTuesday, June 256:00pm - 7:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security