Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Recent breakthroughs in brain-inspired computing promise to address a wide range of
problems from security to healthcare. However, the current strategy of implementing artificial …

This article presents a comprehensive overview of the hardware realizations of artificial
neural network (ANN) models, known as hardware neural networks (HNN), appearing in …

Memristor-based circuit architecture for multilayer neural networks is proposed. It is a first of
its kind demonstrating successful circuit-based learning for multilayer neural network built …

The paper introduces a general class of memristor-based recurrent neural networks with
time-varying delays. Conditions on the nondivergence and global attractivity are established …

Mixed-signal hardware accelerators for deep learning achieve orders of magnitude better
power efficiency than their digital counterparts. In the ultra-low power consumption regime …

A hybrid learning method of a software-based backpropagation learning and a hardware-
based RWC learning is proposed for the development of circuit-based neural networks. The …

In this paper, we propose a hardware friendly On-Chip Training Algorithm for the memristive
Neuromorphic circuits (OCTAN). Although the proposed algorithm has a simple hardware …

Benefiting from area and power efficiency, memristors enable the development of neural
network analog-to-digital converter (ADC) to break through the limitations of conventional …

This brief proposes a general framework of the nonlinear recurrent neural network for
solving online the generalized linear matrix equation (GLME) with global convergence …