论文标题
低功率神经形态EMG手势分类
Low Power Neuromorphic EMG Gesture Classification
论文作者
论文摘要
基于EMG（肌电图）信号的手势识别对于诸如智能可穿戴设备和生物医学神经螺旋控制等应用至关重要。尖峰神经网络（SNN）对于低功率，实时EMG手势识别非常有希望，这是由于其固有的尖峰/事件驱动的时空动力学。在文献中，用于EMG手势分类的神经形态硬件实现（以完整的芯片/板/系统量表）有限。此外，大多数文献尝试利用基于LIF（漏水和火）神经元的原始SNN。在这项工作中，我们通过以下主要贡献解决了上述差距：（1）使用神经形态复发峰值神经网络（RSNN），低功率，高精度演示基于EMG信号的手势识别。特别是，我们提出了一个基于特殊双指数自适应阈值（DEXAT）神经元的多时间量表复发性神经形态系统。我们的网络可实现最先进的分类精度（90％），而在Roshambo EMG数据集中使用的神经元的神经元少约53％。 （2）一种新的多通道尖峰编码方案，用于有效地处理神经形态系统上的实价EMG数据。 （3）显示了在英特尔专用的神经形态loihi芯片上实现复杂自适应神经元的独特多室方法。 （4）在Loihi（Nahuku 32）上实施的RSNN实施可实现〜983x/19x的显着能量/潜伏期益处，而GPU的批量大小为50。
EMG (Electromyograph) signal based gesture recognition can prove vital for applications such as smart wearables and bio-medical neuro-prosthetic control. Spiking Neural Networks (SNNs) are promising for low-power, real-time EMG gesture recognition, owing to their inherent spike/event driven spatio-temporal dynamics. In literature, there are limited demonstrations of neuromorphic hardware implementation (at full chip/board/system scale) for EMG gesture classification. Moreover, most literature attempts exploit primitive SNNs based on LIF (Leaky Integrate and Fire) neurons. In this work, we address the aforementioned gaps with following key contributions: (1) Low-power, high accuracy demonstration of EMG-signal based gesture recognition using neuromorphic Recurrent Spiking Neural Networks (RSNN). In particular, we propose a multi-time scale recurrent neuromorphic system based on special double-exponential adaptive threshold (DEXAT) neurons. Our network achieves state-of-the-art classification accuracy (90%) while using ~53% lesser neurons than best reported prior art on Roshambo EMG dataset. (2) A new multi-channel spike encoder scheme for efficient processing of real-valued EMG data on neuromorphic systems. (3) Unique multi-compartment methodology to implement complex adaptive neurons on Intel's dedicated neuromorphic Loihi chip is shown. (4) RSNN implementation on Loihi (Nahuku 32) achieves significant energy/latency benefits of ~983X/19X compared to GPU for batch size as 50.
