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：

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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。