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Cmos-Memristive Neuromorphic Architecture For Nonlinear Signal Processing, Manu Rathore
Cmos-Memristive Neuromorphic Architecture For Nonlinear Signal Processing, Manu Rathore
Doctoral Dissertations
Neuromorphic computing mimics the functional components and structure of the human brain to achieve highly efficient computing with minimal resources and power consumption. Creating neuromorphic systems in Complementary Metal-Oxide-Semiconductor (CMOS) technology offers an alternative computing paradigm to Von neumann computing. However, implementing these systems on an CMOS Integrated Circuit (IC) poses major challenges. These challenges include implementing synaptic weight multiplication and weight tuning operation that conserves energy and occupies minimal area. Additionally, designing a network-on-chip architecture that is reconfigurable and offers a full-connectivity design space is crucial. Furthermore, implementing a complete architecture for nonlinear data processing and, specifically, online learning …