We survey recent progress in the use of analog memory devices to build neuromorphic
hardware accelerators for deep learning applications. After an overview of deep learning …

Inspired by biology, significant advances have been made in the field of electronic skin
(eSkin) or tactile skin. Many of these advances have come through mimicking the …

With the advent of the Internet of Things era, the detection and recognition of moving objects
is becoming increasingly important. The current motion detection and recognition (MDR) …

Resistive random access memory (ReRAM) is often considered as a strong candidate for
storing the weights in non-von Neumann neuromorphic computing systems. This paper …

Brain-inspired cognitive computing has so far followed two major approaches-one uses
multi-layered artificial neural networks (ANNs) to perform pattern-recognition-related tasks …

The main focus of this paper is the evolution of complex behavior in a system of coupled
nonlinear memristor circuits depending on the applied coupling conditions. Thereby, the …

The conventional von Neumann architecture has been revealed as a major performance
and energy bottleneck for rising data-intensive applications. The decade-old idea of …

Conventional von Neumann architectures cannot successfully meet the demands of
emerging computation and data-intensive applications. These shortcomings can be …

Deep Neural Networks (DNNs) have achieved great progress in academia and industry. But
they have become computational and memory intensive with the increase of network depth …

A neuromorphic system is composed of hardware-based artificial neurons and synaptic
devices, designed to improve the efficiency of neural computations inspired by energy …