Aayush Ankit

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Spiking Neural Network based brain-inspired computing paradigms are becoming increasingly popular tools for various cognitive tasks. The sparse event-driven processing capability enabled by such networks can be potentially appealing for implementation of low-power neural computing platforms. However, the parallel and memory-intensive computations involved(More)
Neuromorphic computing using post-CMOS technologies is gaining immense popularity due to its promising abilities to address the memory and power bottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC - a reconfigurable and energy efficient architecture built-on Memristive Crossbar Arrays (MCA) for deep Spiking Neural Networks(More)
Machine-learning algorithms have shown outstanding image recognition/classification performance for computer vision applications. However, the compute and energy requirement for implementing such classifier models for large-scale problems is quite high. In this paper, we propose Feature Driven Selective Classification (FALCON) inspired by the biological(More)
Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks. However, the recent trend to design Deep Neural Networks (DNNs) for achieving human-like cognitive abilities poses(More)
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