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.

翻译：对于边缘人工智能应用，在资源受限的嵌入式设备上部署在线学习与适应能力，能够应对变化环境中传感器生成的快速数据流。然而，由于在边缘侧维持低延迟和高能效的推理至关重要，设备上的在线学习与适应过程应为推理带来最小的额外开销。基于此目标，我们探索了面向流数据边缘AI应用的、基于脉冲神经网络（SNN）的高能效设备端学习与适应方法。SNN遵循类脑计算原则，如高度并行性、内存与计算单元共址的神经元结构以及事件驱动处理。我们提出了EON-1，一种用于近传感器极端边缘在线特征提取的类脑处理器，它集成了一种快速的在线学习与适应算法。我们报告的结果显示，其学习过程仅带来1%的能耗开销，这是目前相较于其他先进解决方案最低的开销，同时达到了可比的推理精度。此外，我们证明了EON-1能够胜任实时处理高清（HD）与超高清（UHD）流视频的低延迟挑战，并且在学习功能启用时依然如此。