Artificial Intelligence is becoming ubiquitous in products and services that we use daily.
Although the domain of AI has seen substantial improvements over recent years, its …

Memristive devices being applied in neuromorphic computing are envisioned to significantly
improve the power consumption and speed of future computing platforms. The materials …

This chapter addresses opportunities for memristive devices in the framework of
neuromorphic computing. Memristive devices are two-terminal circuit elements, comprising …

Neuromorphic computing takes inspiration from the brain to build highly parallel, energy-and
area-efficient architectures. Recently, hardware realizations of neurons and synapses using …

Oxide-based resistive memory (OxRAM) devices find applications in memory, logic, and
neuromorphic computing systems. Among the different dielectrics proposed in OxRAM …

Memristive devices have been shown to exhibit slow and stochastic resistive switching
behavior under low-voltage, low-current operating conditions. Here we explore such …

This chapter gives a general overview of the state-of-the-art of resistive switching random
access memories (RRAM or ReRAM), which are metal/insulator/metal devices exhibiting …

As a large number of neurons and synapses are needed in spike neural network (SNN)
design, emerging devices have been employed to implement synapses and neurons. In this …

Resistive Random Access Memory (RRAM)-based artificial Neural Networks (NNs) have
been shown to be intrinsically robust to RRAM variability but no study has been done to …

Memristive devices have found application in both random access memory and
neuromorphic circuits. In particular, it is known that their behavior resembles that of neuronal …