neuromorphic computing

Energy-Efficient Neuromorphic Computing Systems

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Neuromorphic computing has emerged as a new and promising computing principle that emulates how human brains process information. The underlying spiking neural networks (SNNs) are well-known for having higher energy efficiency than artificial neural networks (ANNs). Neuromorphic systems enable highly parallel computation and reduce memory bandwidth limitations, making hardware performance scalable and sustainable given the ever-increasing complexities of artificial intelligence (AI). Inefficiency in the design of a neuromorphic system generally originates from redundant parameters, nonoptimized models, a lack of computing parallelism, and inefficient training algorithms. This dissertation aims to address these problems and propose effective solutions.

Multifunctional memory devices and their applications in neuromorphic computing

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The traditional trajectory of electronic device scaling, guided by Moore's law, is currently encountering physical limitations. To address this, the "More-than-Moore" (MtM) trend has emerged, emphasizing the diversification of device functionalities to include sensing, storing, and processing data.

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

Mohammed Affan Zidan

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
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

Dayanand Kumar

Postdoctoral Research Fellow, Electrical and Computer Engineering
Dr. Kumar is currently working as a postdoctoral fellow at SAMA lab. He received his M. Tech. from Indian Institute of Technology (IIT), Varanasi, India, in 2012, and his Ph.D. from National Chiao Tung University (NCTU), Taiwan, in 2018. He was awarded by the President of Taiwan for outstanding academic research performance during his Ph.D. in 2016. After his Ph.D., he worked as a Principal Integration Engineer (PIE) in Micron Technology, Taiwan, from 2018 to 2019. Then he moved to Nanyang Technological University (NTU), Singapore, as a postdoctoral research fellow. Later he joined NCTU