Presentation
Multi-modal Signal applied Dynamic neuron based Spike processor for Stress Detection
DescriptionSpiking 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 in this work. Execution of the SNNs on Intel Loihi-2 (a neuromorphic research chip) showed 5× to 83× better energy-delay product (EDP) compared to equivalent artificial neural networks (ANNs) implemented on a low-power edge-GPU, and a marginal gain of 1.3× to 2.6× over Spiking Quantized Neural Network (SQNN) equipped with Dynamic Adaptive Leaky integrate and Fire neurons (DALIF) and Dynamic Adaptive Current based Leaky integrate and Fire neurons (DACLIF). Significant EDP gain (83×), supplemented with fast inference rate (∼ 9×) were reported for the multi-modal SNN, which is composed of ∼ 9× and ∼ 1.8× less parameters in comparison with the corresponding ANN run on an edge-GPU and SQNN run on FPGA respectively.
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