spiking neural networks

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

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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

Wenzhe Guo

Wenzhe Guo is currently a PhD student at the Sensors Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST). Education and Early Career Wenzhe Guo received his B.S. majored in integrated circuits design from University of Electronic Science and Technology of China (UESTC) in 2017. He got admitted to KAUST and obtained MS degree at Electrical Engineering department in KAUST in 2018. Research Interests My research interests lie in design and implementation of brain-inspired computational algorithm, exploration of neuromorphic computing