Power-Accuracy Trade-Offs for Heartbeat Classification on Neural Networks Hardware
Power-Accuracy Trade-Offs for Heartbeat Classification on Neural Networks Hardware
复制标题
神经网络硬件上心跳分类的功率精度权衡
DOI：
10.1166/jolpe.2018.1582
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
F. Catthoor
中科院分区：
文献类型：
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作者：
Adarsha Balaji;Federico Corradi;Anup Das;S. Pande;S. Schaafsma;F. Catthoor
使用心电图（ECG）数据的心跳分类是现代可穿戴设备的重要特征。计算资源和权力使CNN在资源限制的可穿戴设备上进行映射。使用尖峰神经网络（SNN）的分类，这是一种基于生物学启发的事件驱动的神经网络的替代方法。但是，由于涉及峰值的复杂错误，我们提出了一种替代方法。对于基于SNN的心跳分类，我们以优化的CNN实现心跳分类任务，然后将CNN操作（例如多重占用，合并和SoftMax）转换为相当于峰值的情况。 - 基于马萨诸塞州技术研究所和贝丝以色列医院（MIT/BIH）的公开可用的心电图数据库的基于心跳分类，并证明了最小的与基于CNN的Hearbeat分类相比，准确性的损失与CNN神经元相比，每个层中的SNN神经元的激活都很少，我们还显示了同一层中的SNN神经元的激活。这种稀疏性随着神经网络的层数增加而增加，我们详细介绍了SNN的功率准确性权衡，并显示了SNN的87.76％和96.82％与仅CNN的实施相比，神经元和突触活动尤其是准确性损失在0.6％至1.00％之间。
Heartbeat classification using electrocardiogram (ECG) data is an essential feature of modern day wearable devices. State-of-the-art machine learning-based heartbeat classifiers are designed using convolutional neural networks (CNN). Despite their high classification accuracy, CNNs require significant computational resources and power. This makes the mapping of CNNs on resourceand power-constrained wearable devices challenging. In this paper, we propose heartbeat classification using spiking neural networks (SNN), an alternative approach based on a biologically inspired, event-driven neural networks. SNNs compute and transfer information using discrete spikes that require fewer operations and less complex hardware resources, making them energy-efficient compared to CNNs. However, due to complex error-backpropagation involving spikes, supervised learning of deep SNNs remains challenging. We propose an alternative approach to SNN-based heartbeat classification. We start with an optimized CNN implementation of the heartbeat classification task and then convert the CNN operations, such as multiply-accumulate, pooling and softmax, into spiking equivalent with a minimal loss of accuracy. We evaluate the SNN-based heartbeat classification using publicly available ECG database of the Massachusetts Institute of Technology and Beth Israel Hospital (MIT/BIH), and demonstrate a minimal loss in accuracy when compared to 85.92% accuracy of a CNN-based hearbeat classification. We demonstrate that, for every operation, the activation of SNN neurons in each layer is sparse when compared to CNN neurons, in the same layer. We also show that this sparsity increases with an increase in the number of layers of the neural network. In addition, we detail the power-accuracy trade-off of the SNN and show a 87.76% and 96.82% reduction in SNN neuron and synapse activity,respectively, for accuracy loss ranging between 0.6% and 1.00%, when compared to a CNN-only implementation. Keywords– Heartbeat classification, spiking neural network(SNN), convolution neural network(CNN)