EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction
This addresses the need for low-latency, power-efficient edge AI applications, though it appears incremental as it builds on existing spiking neural network methods with specific hardware optimizations.
The paper tackles the problem of enabling online learning and adaptation on resource-constrained edge devices for streaming data, proposing EON-1, a brain-inspired processor that achieves only 1% energy overhead for learning while maintaining comparable inference accuracy and handling HD/UHD video in real-time.
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.