ArticleFrontiers in neuroscience2019
A Soft-Pruning Method Applied During Training of Spiking Neural Networks for In-memory Computing Applications.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Dynamic spatio-temporal pruning for efficient spiking neural networks.Frontiers in neuroscience · 2025Article
- Mental stress recognition on the fly using neuroplasticity spiking neural networks.Scientific reports · 2023Article
- STDP-Driven Rewiring in Spiking Neural Networks under Stimulus-Induced and Spontaneous Activity.Biomimetics (Basel, Switzerland) · 2023Article
- Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks.Frontiers in neuroscience · 2023Article
- Backpropagation With Sparsity Regularization for Spiking Neural Network Learning.Frontiers in neuroscience · 2022Article
- Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility.Frontiers in neuroscience · 2022Article
- Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems.Frontiers in neuroscience · 2021Article
- Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems.Frontiers in neuroscience · 2020Article
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5 authors.
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Abstract
Inspired from the computational efficiency of the biological brain, spiking neural networks (SNNs) emulate biological neural networks, neural codes, dynamics, and circuitry. SNNs show great potential for the implementation of unsupervised learning using in-memory computing. Here, we report an algorithmic optimization that improves energy efficiency of online learning with SNNs on emerging non-volatile memory (eNVM) devices. We develop a pruning method for SNNs by exploiting the output firing characteristics of neurons. Our pruning method can be applied during network training, which is different from previous approaches in the literature that employ pruning on already-trained networks. This approach prevents unnecessary updates of network parameters during training. This algorithmic optimization can complement the energy efficiency of eNVM technology, which offers a unique in-memory computing platform for the parallelization of neural network operations. Our SNN maintains ~90% classification accuracy on the MNIST dataset with up to ~75% pruning, significantly reducing the number of weight updates. The SNN and pruning scheme developed in this work can pave the way toward applications of eNVM based neuro-inspired systems for energy efficient online learning in low power applications.
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