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

2021/05/08 by Amar Shrestha, Shrestha, Amar, Haowen Fang +7 · 2 citations
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Artificial intelligence #Artificial neural network #Backpropagation #Computer architecture #Computer engineering #Computer science #Constraint (computer-aided design) #Distributed #Emerging Technologies (cs.ET) #Engineering #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #MNIST database #Machine learning #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #Neuromorphic engineering #Parallel #Spike (software development) #Spiking neural network #and Cluster Computing (cs.DC) #cs.DC #cs.ET #cs.NE

paper · pdf · doi:10.48550/arxiv.2105.03649

published in arXiv (Cornell University) (Cornell University) · 6 pages, 5 figures, accepted for Design Automation Conference (DAC) 2021

arxiv created 2021/05/08 · openalex publication_date 2021/05/08 · arxiv updated 2021/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

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.

Citations

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks
• Learning Multiple Layers of Features from Tiny Images

Cited by

• Frozen Backpropagation: Relaxing Weight Symmetry in Deep Spiking Neural Networks
• Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware

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• BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks
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• Mapping high-performance RNNs to in-memory neuromorphic chips