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

arXiv: https://arxiv.org/abs/2105.03649 · HTML: https://mlsanity.com/paper/2105.03649/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2021.

## Built on by (most cited first, 11 of 11)

- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [Neuromorphic Computing and Sensing in Space](https://mlsanity.com/paper/2212.05236/) (2022)
- [Design Principles for Lifelong Learning AI Accelerators](https://mlsanity.com/paper/2310.04467/) (2023)
- [Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks](https://mlsanity.com/paper/2308.02194/) (2023)
- [Neuronal Competition Groups with Supervised STDP for Spike-Based Classification](https://mlsanity.com/paper/2410.17066/) (2024)
- [Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning](https://mlsanity.com/paper/2301.07275/) (2023)
- [Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks](https://mlsanity.com/paper/2311.16141/) (2023)
- [SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2404.19668/) (2024)
- [A temporally and spatially local spike-based backpropagation algorithm to enable training in hardware](https://mlsanity.com/paper/2207.09755/) (2022)
- [Forward Direct Feedback Alignment for Online Gradient Estimates of Spiking Neural Networks](https://mlsanity.com/paper/2403.08804/) (2024)
- [Frozen Backpropagation: Relaxing Weight Symmetry in Deep Spiking Neural Networks](https://mlsanity.com/paper/2505.13741/) (2025)

## Abstract (arXiv)

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.