Nonvolatile redox transistors (NVRTs) based upon Li-ion battery materials are demonstrated
as memory elements for neuromorphic computer architectures with multi-level analog …

Neuromorphic computers based on analogue neural networks aim to substantially lower
computing power by reducing the need to shuttle data between memory and logic units …

Recent progress in deep learning extends the capability of artificial intelligence to various
practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis …

This paper gives an overview of recent progress in the brain-inspired computing field with a
focus on implementation using emerging memories as electronic synapses. Design …

Electronic synaptic devices are important building blocks for neuromorphic computational
systems that can go beyond the constraints of von Neumann architecture. Although two …

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional
computing through parallel programming and readout of artificial neural network weights in …

We demonstrate a nonvolatile Electro-Chemical Random-Access Memory (ECRAM) based
on lithium (Li) ion intercalation in tungsten oxide (WO 3) for high-speed, low-power …

Functional emulation of biological synapses using electronic devices is regarded as the first
step toward neuromorphic engineering and artificial neural networks (ANNs). Electrolyte …

To inaugurate energy-efficient hardware as a solution to complex tasks, information
processing paradigms shift from von Neumann to non-von Neumann computing …

Lithium nanoionic transistors have recently emerged as promising artificial synaptic devices
for neuromorphic hardware systems. However, mimicking the essential synaptic …