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Power and precise scaling are significant restraining elements to advancements in computing. This paper presents a power efficient memristive device technology in the design of a neuromorphic architectural model that promises to overcome many of the performance limitations of conventional Von Neumann systems. The resulting memristive Dynamic Adaptive Neural(More)
Current Deep Learning approaches have been very successful using convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers. Three limitations of this approach are: 1) they are based on a simple layered network topology, i.e., highly connected layers, without intra-layer connections; 2) the networks are manually(More)
While neuromorphic computing offers methods to solve complex problems, current software-based networks offer limited flexibility and potential for low-power implementations. The memristive dynamic adaptive neural network array (mrDANNA) is a flexible hardwarebased system, with applications including, but not limited to real-time speech recognition and(More)
Memristors are widely leveraged in neuromorphic systems for constructing synapses. Resistance switching characteristics of memristors enable online learning in synapses. This paper addresses a fundamental issue associated with the design of synapses with memristors whose switching rates in either direction differ up to two orders of magnitude. A(More)
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