EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction
<|reference_start|>EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction: 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 principles of brain-inspired computing, such as high-parallelism, neuron co-located memory and compute, and event-driven processing. We propose EON-1, a brain-inspired processor for near-sensor extreme edge online feature extraction, that integrates a fast online learning and adaptation algorithm. We report results of only 1% energy overhead for learning, by far the lowest overhead when compared to other SoTA solutions, while attaining comparable inference accuracy. Furthermore, we demonstrate that EON-1 is up for the challenge of low-latency processing of HD and UHD streaming video in real-time, with learning enabled.<|reference_end|>
@article{dobrita2024eon-1:,
    title={EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online
  Feature Extraction},
    author={Alexandra Dobrita (1 and 2), Amirreza Yousefzadeh (1), Simon Thorpe
  (3), Kanishkan Vadivel (1), Paul Detterer (1), Guangzhi Tang (1), Gert-Jan
  van Schaik (1), Mario Konijnenburg (1), Anteneh Gebregiorgis (2), Said
  Hamdioui (2), Manolis Sifalakis (1) ((1) Imec Netherlands, (2) Delft
  University of Technology, (3) University of Toulouse)},
    journal={arXiv preprint arXiv:2406.17285},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17285},
    primaryClass={cs.NE cs.AI cs.ET cs.LG}
}