论文标题
可穿戴设备的脑电图分类中的多级二进制LSTM
Multi-level Binarized LSTM in EEG Classification for Wearable Devices
论文作者
论文摘要
长期记忆（LSTM）在各种顺序应用中广泛使用。由于大量的计算和内存要求，复杂的LSTM几乎不可部署在可穿戴和资源有限的设备上。引入了二进制LSTM，以应对此问题，但是，在某些应用中，例如EEG分类，它们会导致明显的准确性损失，例如EEG分类，这是在可穿戴设备中部署的必不可少的。在本文中，我们提出了有效的多级二进制LSTM，该LSTM大大降低了计算，同时确保准确性接近完全精确的LSTM。通过部署5级二进制的权重和输入，我们的方法分别在65nm技术中降低了MAC运行的面积和延迟约为31*和27*，精度损失小于0.01％。与许多计算密集的深度学习方法相反，所提出的算法是轻量级的，因此，具有准确的基于LSTM的EEG分类为实时可穿戴设备带来了性能效率。
Long Short-Term Memory (LSTM) is widely used in various sequential applications. Complex LSTMs could be hardly deployed on wearable and resourced-limited devices due to the huge amount of computations and memory requirements. Binary LSTMs are introduced to cope with this problem, however, they lead to significant accuracy loss in some application such as EEG classification which is essential to be deployed in wearable devices. In this paper, we propose an efficient multi-level binarized LSTM which has significantly reduced computations whereas ensuring an accuracy pretty close to full precision LSTM. By deploying 5-level binarized weights and inputs, our method reduces area and delay of MAC operation about 31* and 27* in 65nm technology, respectively with less than 0.01% accuracy loss. In contrast to many compute-intensive deep-learning approaches, the proposed algorithm is lightweight, and therefore, brings performance efficiency with accurate LSTM-based EEG classification to real-time wearable devices.
