Recently, due to the availability of big data and the rapid growth of computing power,
artificial intelligence (AI) has regained tremendous attention and investment. Machine …

As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …

In this article, we review the existing analog resistive switching memory (RSM) devices and
their hardware technologies for in-memory learning, as well as their challenges and …

Memristor-based synaptic network has been widely investigated and applied to
neuromorphic computing systems for the fast computation and low design cost. As …

Deep Neural Networks (DNNs) have shown significant advantages in many domains, such
as pattern recognition, prediction, and control optimization. The edge computing demand in …

As one of most fascinating machine learning techniques, deep neural network (DNN) has
demonstrated excellent performance in various intelligent tasks such as image classification …

Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer
and parallelize on-chip computations for neural networks. Here, we report a …

Current deep learning approaches have been very successful using convolutional neural
networks trained on large graphical-processing-unit-based computers. Three limitations of …

The advances in the field of machine learning using neuromorphic systems have paved the
pathway for extensive research on possibilities of hardware implementations of neural …

We investigate the use and performance of the quasi-static memdiode model (QMM) when
incorporated into large cross-point arrays intended for pattern classification tasks. Following …