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

• URL: http://arxiv.org/abs/2406.17285v1
• Date: Tue, 25 Jun 2024 05:23:41 GMT
• Title: EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction
• Authors: Alexandra Dobrita, Amirreza Yousefzadeh, Simon Thorpe, Kanishkan Vadivel, Paul Detterer, Guangzhi Tang, Gert-Jan van Schaik, Mario Konijnenburg, Anteneh Gebregiorgis, Said Hamdioui, Manolis Sifalakis,
• Abstract summary: EON-1 is a brain-inspired processor for near-sensor extreme edge online feature extraction. We report results of only 1% energy overhead for learning, by far the lowest overhead when compared to other SoTA solutions.
• Score: 32.343120409334475
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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