论文标题
在线上几个关于神经形态处理器的手势学习
Online Few-shot Gesture Learning on a Neuromorphic Processor
论文作者
论文摘要
我们介绍了在线错误触发的学习（SOEL）系统，用于在线上，几乎没有神经形态处理器学习。 Soel学习系统结合了转移学习和计算神经科学和深度学习原理。我们表明，在神经形态硬件上实施的部分训练的深尖峰神经网络（SNN）可以在线迅速适应域内的新数据类别。发生错误时，Soel更新触发器，可以更快地学习，更新更新。使用手势识别作为案例研究，我们表明Soel可用于在线学习新类的预录用手势数据，并快速在线学习从数据流的现场直播新的手势，从动态的活动像素视觉传感器到Intel Loihi Neurohi Neuromorphichormorphic研究处理器。
We present the Surrogate-gradient Online Error-triggered Learning (SOEL) system for online few-shot learning on neuromorphic processors. The SOEL learning system uses a combination of transfer learning and principles of computational neuroscience and deep learning. We show that partially trained deep Spiking Neural Networks (SNNs) implemented on neuromorphic hardware can rapidly adapt online to new classes of data within a domain. SOEL updates trigger when an error occurs, enabling faster learning with fewer updates. Using gesture recognition as a case study, we show SOEL can be used for online few-shot learning of new classes of pre-recorded gesture data and rapid online learning of new gestures from data streamed live from a Dynamic Active-pixel Vision Sensor to an Intel Loihi neuromorphic research processor.
