In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor

• URL: http://arxiv.org/abs/2105.03649v1
• Date: Sat, 8 May 2021 09:22:21 GMT
• Title: In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor
• Authors: Amar Shrestha, Haowen Fang, Daniel Patrick Rider, Zaidao Mei and Qinru Qiu
• Abstract summary: 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.
• Score: 6.816315761266531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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