Conventional von Newmann-based computers face severe challenges in the processing
and storage of the large quantities of data being generated in the current era of “big data.” …

Character recognition from handwritten images has received greater attention in research
community of pattern recognition due to vast applications and ambiguity in learning …

Neuromorphic computing, drawing inspiration from the brain, stands out for its high energy
efficiency in executing complex tasks. Memristive device-based neuromorphic computing …

The Artificial Intelligence (AI) in edge computing is requesting new processing units with a
much higher computing-power ratio. The emerging resistive Non-Volatile Memory (NVM) …

In this paper, we devise and optimize schemes for the resistive random-access memory
(RRAM)-based hardware implementation of convolutional neural networks (CNNs). The key …

Due to the dynamic characteristics, memristors have great potential for implementing various
neural network training and applications. By applying memristors to neural networks as a …

Handwritten digit recognition is a well-researched area in the field of pattern recognition that
is used for distinguishing the pre-segmented handwritten digits. Deep learning is a recent …

Memristors have emerged as a potential tool to implement the training and operation of an
integrated neural network, because of its current-voltage curve of the hysteresis loop and …

Significant improvements in the accuracy of Neural Networks (NNs) have been observed for
a wide range of problems, often achieved by highly over-parameterized models. Despite the …

The rapidly increasing sophistication of cyber threats occurring in parallel with our growing
reliance on networked systems for everything from shopping to managing critical …