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In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor
arXiv - CS - Emerging Technologies Pub Date : 2021-05-08 , DOI: arxiv-2105.03649
Amar Shrestha, Haowen Fang, Daniel Patrick Rider, Zaidao Mei, Qinru Qiu

Although widely used in machine learning, backpropagation cannot directly be applied to SNN training and is not feasible on a neuromorphic processor that emulates biological neuron and synapses. This work presents a spike-based backpropagation algorithm with biological plausible local update rules and adapts it to fit the constraint in a neuromorphic hardware. The algorithm is implemented on Intel Loihi chip enabling low power in-hardware supervised online learning of multilayered SNNs for mobile applications. We test this implementation on MNIST, Fashion-MNIST, CIFAR-10 and MSTAR datasets with promising performance and energy-efficiency, and demonstrate a possibility of incremental online learning with the implementation.

中文翻译:

在神经形态处理器上的多层尖峰神经网络的硬件内学习

尽管反向传播在机器学习中得到了广泛的应用,但是反向传播不能直接应用于SNN训练,并且在模拟生物神经元和突触的神经形态处理器上也不可行。这项工作提出了一种基于峰值的反向传播算法,该算法具有生物学上合理的局部更新规则,并对其进行了调整,以适应神经形态硬件中的约束。该算法在Intel Loihi芯片上实现,可在移动应用程序的低功耗硬件监督下对多层SNN进行在线学习。我们在具有良好性能和能效的MNIST,Fashion-MNIST,CIFAR-10和MSTAR数据集上测试了此实现,并演示了该实现进行增量在线学习的可能性。
更新日期:2021-05-11
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