论文标题
一种基于时间编码的多层尖峰神经网络的监督学习算法
A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design
论文作者
论文摘要
尖峰神经网络（SNN）是脑启发的数学模型，具有以尖峰形式处理信息的能力。预计SNN不仅可以提供新的机器学习算法，还可以提供在VLSI电路中实现的能节能计算模型。在本文中，我们提出了一种基于时间编码的新型监督学习算法。该算法中的尖刺神经元旨在促进具有模拟电阻记忆的模拟VLSI实现，从而可以实现超高的能量效率。我们还提出了几种技术来提高识别任务的性能，并表明所提出的算法的分类准确性与MNIST数据集上最新的时间编码SNN算法的分类准确性高。最后，我们讨论了拟议的SNN对设备制造过程产生的变化的鲁棒性，并且在模拟VLSI实现中不可避免。我们还提出了一种技术来抑制制造过程中变化对识别性能的影响。
Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms, but also energy-efficient computational models when implemented in VLSI circuits. In this paper, we propose a novel supervised learning algorithm for SNNs based on temporal coding. A spiking neuron in this algorithm is designed to facilitate analog VLSI implementations with analog resistive memory, by which ultra-high energy efficiency can be achieved. We also propose several techniques to improve the performance on a recognition task, and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST dataset. Finally, we discuss the robustness of the proposed SNNs against variations that arise from the device manufacturing process and are unavoidable in analog VLSI implementation. We also propose a technique to suppress the effects of variations in the manufacturing process on the recognition performance.
