Dr. Abdullah is currently a faculty member at the Department of Electrical Engineering, University of Baghdad. He specializes in digital and mixed-signal designs and his current research interests include neuromorphic architectures for energy constrained platforms and biologically inspired algorithms. Dr. Abdullah received the B.Sc. degree in Electrical Engineering from the University of Baghdad, Iraq, in 2009. Subsequently, he pursued the M.Sc. degree in the same discipline from Rochester Institute of Technology, USA, in 2015, where he also received his Ph.D. degree in Electrical and Computer Engineering in 2020. Prior to his current role at University of Baghdad, Dr. Abdullah worked as a Research Scientist within the Electrical and Computer Engineering Department, University of Texas, TX, USA. He also worked as data and image processing engineer at Seagate Technology, MN, USA.
- PhD in Electrical and Computer Engineering
- MSc in Electrical and Microelectronics Engineering
- BSc in Electrical Engineering
- Digital and mixed-signal designs
- Neuromorphic Systems for resource-constrained platforms
- Biologically inspired algorithms
- Machine learning
- Digital Systems Design
- Deep Learning
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Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil
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