Gangotree Chakma

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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)
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)
Neuromorphic computing is a promising post-Moore's law era technology. A wide variety of neuromorphic computer (NC) architectures have emerged in recent years, ranging from traditional fully digital CMOS to nanoscale implementations with novel, beyond CMOS components. There are already major questions associated with how we are going to program and use NCs(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)
In this paper we present a memristive neuromorphic system for higher power and area efficiency. The system is based on a mixed signal approach considering the digital nature of the peripheral and control logics and the integration being analog. So, the system is connected digitally outside but the core is purely analog. This mixed signal approach provides(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)
In this paper we present circuit techniques to optimize analog neurons specifically for operation in memristive neuromorphic systems. Since the peripheral circuits and control signals of the system are digital in nature, we take a mixed-signal circuit design approach to leverage analog computation in multiplying and accumulating digital input spikes and(More)
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