Online Supervised Learning for Hardware-Based Multilayer Spiking Neural Networks Through the Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity
Online Supervised Learning for Hardware-Based Multilayer Spiking Neural Networks Through the Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity
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DOI：
10.1109/tnnls.2017.2761335
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发表时间：
2018-09
影响因子：
10.4
通讯作者：
Nan Zheng;P. Mazumder
中科院分区：
文献类型：
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作者：
Nan Zheng;P. Mazumder
本文提出了一种多层脉冲神经网络（SNNs）的在线监督学习算法。发现SNN中神经元的尖峰定时可以用来估计与每个突触相关联的梯度。利用所提出的估计梯度的方法，可以实现类似于在常规人工神经网络（ANN）中采用的随机梯度下降过程的学习。除了传统的逐层反向传播，一个单程直接反向传播是可能的，使用所提出的学习算法。两个神经网络，一个和两个隐藏层，作为例子来证明所提出的学习算法的有效性。讨论了几种用于更有效学习的技术，包括利用随机不应期来避免尖峰饱和，采用量化噪声注入技术和伪随机初始条件来解相关尖峰定时，以及利用SNN中的渐进精度来减少推理延迟和能量。进行了大量的参数模拟，以检查上述技术。该学习算法的开发考虑到硬件实现的方便性和与经典的基于人工神经网络的学习相对兼容。因此，该算法不仅在其专用硬件上具有SNN的高能效和良好的可扩展性，而且还受益于传统的基于ANN的学习的成熟理论和技术。最后，通过美国国家标准与技术研究所数据库基准测试，验证了该学习算法的有效性。单隐层和双隐层神经网络的分类正确率分别为97.2%和97.8%。此外，硬件实现的两个主流架构的简要讨论。
In this paper, we propose an online learning algorithm for supervised learning in multilayer spiking neural networks (SNNs). It is found that the spike timings of neurons in an SNN can be exploited to estimate the gradients that are associated with each synapse. With the proposed method of estimating gradients, learning similar to the stochastic gradient descent process employed in a conventional artificial neural network (ANN) can be achieved. In addition to the conventional layer-by-layer backpropagation, a one-pass direct backpropagation is possible using the proposed learning algorithm. Two neural networks, with one and two hidden layers, are employed as examples to demonstrate the effectiveness of the proposed learning algorithms. Several techniques for more effective learning are discussed, including utilizing a random refractory period to avoid saturation of spikes, employing a quantization noise injection technique and pseudorandom initial conditions to decorrelate spike timings, in addition to leveraging the progressive precision in an SNN to reduce the inference latency and energy. Extensive parametric simulations are conducted to examine the aforementioned techniques. The learning algorithm is developed with the considerations of ease of hardware implementation and relative compatibility with the classic ANN-based learning. Therefore, the proposed algorithm not only enjoys the high energy efficiency and good scalability of an SNN in its specialized hardware but also benefits from the well-developed theory and techniques of conventional ANN-based learning. The Modified National Institute of Standards and Technology database benchmark test is conducted to verify the newly proposed learning algorithm. Classification correct rates of 97.2% and 97.8% are achieved for the one-hidden-layer and two-hidden-layer neural networks, respectively. Moreover, a brief discussion of the hardware implementations is presented for two mainstream architectures.