neuromorphic computing

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

Mohammed Affan Zidan

Ph.D., Electrical and Computer Engineering

memristor Memory chaotic systems ray-tracing computer arithmetic neuromorphic computing

Mohammed Affan Zidan obtained his Ph.D. Degree in the Electrical Engineering (EE) Program from King Abdullah University of Science and Technology, under the supervision of Prof. Khaled Nabil Salama. Mohammed Zidan earned his M.Sc. in Electronics and Communications Engineering from Faculty of Engineering, Cairo University, Egypt in 2010, where he was ranked first. In 2006, he received his B.Sc. in Electronics and Communications Engineering, Institute of Aviation Engineering and Technology (IAET), Egypt, also ranking first. Before joining KAUST, he worked as a teaching assistant at the German

Hasan Erdem Yantir

Postdoctoral Research Fellow, Sensors

neuromorphic computing In-memory computing reconfigurable computing

Education and early career Hasan Erdem Yantır received the Ph.D. degree in Computer Engineering from the University of California, Irvine, the US and the M.Sc. degree in Computer Engineering from Bogazici University, Istanbul, Turkey. He was an engineer at Ericsson from 2011 to 2012 and at Ford Motor Company from 2012 to 2013. He is currently a postdoctoral research fellow at King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. Research Interests Hasan’s research interests include artificial intelligence, digital IC design, in-memory computing, computer architectures, and