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.

在边缘人工智能应用中，使用脉冲神经网络（SNNs）进行设备上的在线学习和自适应，以实现对不断变化的环境中产生的传感器数据的快速处理。我们提出了一种脉冲神经网络的脑启发式处理器EON-1，用于在近传感器极端边缘在线特征提取，该处理器集成了快速的在线学习和自适应算法。我们报告了仅1%的学习能耗开销，远低于其他SoTA解决方案，同时达到相当的推理准确度。此外，我们证明了EON-1可以在实时处理高清和超高清流媒体视频时实现低延迟，同时进行学习。

EON-1：一种用于近传感器极端边缘在线特征提取的脑启发处理器