The translation of emerging application concepts that exploit resistive random access
memory (ReRAM) into large-scale practical systems requires realistic yet computationally …

Memristors, emerging non-volatile memory devices, have shown promising potential in
neuromorphic hardware designs, especially in spiking neural network (SNN) hardware …

Memristive technology has experienced explosive growth in the last decade, with multiple
device structures being developed for a wide range of applications. However, transitioning …

Emerging two-terminal nanoscale memory devices, known as memristors, have
demonstrated great potential for implementing energy-efficient neuro-inspired computing …

In this paper, we endeavor to evaluate and model switching noise in resistive random
access memory (RRAM) devices. Although noise is always present in physical systems, the …

Reservoir computing (RC) offers a neuromorphic framework that is particularly effective for
processing spatiotemporal signals. Known for its temporal processing prowess, RC …

RRAM technology has experienced explosive growth in the last decade, with multiple device
structures being developed for a wide range of applications. However, transitioning the …

Memristors are among the most promising devices for building neural processors and non-
volatile memory. One circuit design stage involves modeling, which includes the option of …

Memristor devices have diverse physical models depending on their structure. In addition,
the physical properties of memristors are described using complex differential equations …

A new method to implement threshold logic functions using memristors is presented. This
method benefits from the high range of memristor's resistivity, which is used to define …