论文标题
使用神经形态硬件对美国手语的静态手势识别
Static Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware
论文作者
论文摘要
在本文中，我们为两个静态的美国手语（ASL）手势分类任务（即ASL Alphabet和ASL Digits）开发了四个尖峰神经网络（SNN）模型。 SNN模型部署在英特尔的神经形态平台上，然后与部署在边缘计算设备（Intel神经计算棒2（NCS2））上的等效深神经网络（DNN）模型进行了比较。在准确性，延迟，功耗和能源方面，我们在两个系统之间进行了全面的比较。最好的DNN模型在ASL字母数据集上的精度为99.93％，而最佳性能SNN模型的精度为99.30％。对于ASL数字数据集，最佳DNN模型的精度为99.76％，而SNN的精度达到99.03％。此外，我们获得的实验结果表明，与NCS2相比，Loihi神经形态硬件的实现分别可分别降低20.64倍和4.10倍。
In this paper, we develop four spiking neural network (SNN) models for two static American Sign Language (ASL) hand gesture classification tasks, i.e., the ASL Alphabet and ASL Digits. The SNN models are deployed on Intel's neuromorphic platform, Loihi, and then compared against equivalent deep neural network (DNN) models deployed on an edge computing device, the Intel Neural Compute Stick 2 (NCS2). We perform a comprehensive comparison between the two systems in terms of accuracy, latency, power consumption, and energy. The best DNN model achieves an accuracy of 99.93% on the ASL Alphabet dataset, whereas the best performing SNN model has an accuracy of 99.30%. For the ASL-Digits dataset, the best DNN model achieves an accuracy of 99.76% accuracy while the SNN achieves 99.03%. Moreover, our obtained experimental results show that the Loihi neuromorphic hardware implementations achieve up to 20.64x and 4.10x reduction in power consumption and energy, respectively, when compared to NCS2.
