In this paper, we present a survey of recent works in developing neuromorphic or neuro-
inspired hardware systems. In particular, we focus on those systems which can either learn …

Although Deep Neural Networks (DNN) are ubiquitously utilized in many applications, it is
generally difficult to deploy DNNs on resource-constrained devices, eg, mobile platforms …

Neuromorphic hardware implements biological neurons and synapses to execute a spiking
neural network (SNN)-based machine learning. We present SpiNeMap, a design …

Neuromorphic computing was originally referred to as the hardware that mimics neuro-
biological architectures to implement models of neural systems. The concept was then …

Resistive crossbars designed with nonvolatile memory devices have emerged as promising
building blocks for deep neural network (DNN) hardware, due to their ability to compactly …

Spiking Neural Networks (SNNs) are an emerging computation model that uses event-
driven activation and bio-inspired learning algorithms. SNN-based machine learning …

Machine learning applications that are implemented with spike-based computation model,
eg, Spiking Neural Network (SNN), have a great potential to lower the energy consumption …

Memristors have recently received significant attention as device-level components for
building a novel generation of computing systems. These devices have many promising …

Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-
Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged …

Neuromorphic architectures implement biological neurons and synapses to execute
machine learning algorithms with spiking neurons and bio-inspired learning algorithms …