EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction

arXiv:2406.17285v1 Announce Type: cross
Abstract: For Edge AI applications, deploying online learning and adaptation on resource-constrained embedded devices can deal with fast sensor-generated streams of data in changing environments. However, since maintaining low-latency and power-efficient inference is paramount at the Edge, online learning and adaptation on the device should impose minimal additional overhead for inference. With this goal in mind, we explore energy-efficient learning and adaptation on-device for streaming-data Edge AI applications using Spiking Neural Networks (SNNs), which follow the …

abstract ai applications applications arxiv brain brain-inspired cs.ai cs.et cs.lg cs.ne data deal deploying device devices edge edge ai embedded embedded devices environments extraction feature feature extraction generated however inference latency low near online learning power processor sensor the edge type