unsupervised learning

Unsupervised adaptive weight pruning for energy-efficient neuromorphic systems Frontiers in Neuroscience, section Neuromorphic Engineering.

1 min read · Thu, Nov 26 2020

pruning Circuits unsupervised learning spiking neural networks Pattern Recognition neuromorphic computing STDP

Wenzhe Guo, et al., "Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems." Frontiers in Neuroscience 14, 2020, 1189. To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems. In this work, we propose an unsupervised online adaptive weight pruning method that dynamically removes non-critical weights from a spiking neural network (SNN) to reduce network complexity and improve