Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …

Growth of data eases the way to access the world but requires increasing amounts of energy
to store and process. Neuromorphic electronics has emerged in the last decade, inspired by …

We propose a hardware-friendly architecture of a convolutional neural network using a 32×
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …

CMOS-RRAM integration holds great promise for low energy and high throughput
neuromorphic computing. However, most RRAM technologies relying on filamentary …

Memristive devices, known for their nonvolatile resistive switching, are promising
components for next-generation neuromorphic computing systems, which mimic the brain's …

This paper suggests the practical implications of utilizing a high-density crossbar array with
self-compliance (SC) at the conductive filament (CF) formation stage. By limiting the …

Resistive random-access memory (RRAM) is a promising candidate for next-generation
nonvolatile memory (NVM). Furthermore, RRAM is highly suitable for integration as a …

Memristor devices can be utilized for various computing applications, and stochastic
computing is one of them. The intrinsic stochastic characteristics of the memristor cause …

Memory devices are an essential part of modern electronics. Efforts to move beyond the
traditional “read” and “write” of digital information in volatile and non‐volatile memory …

On-chip learning in neuromorphic systems, wherein both training and inference are
performed on memristive synaptic devices, has been actively studied recently. However, on …