Resistive RAM (RRAM) is a promising technology to replace traditional technologies such
as Flash, because of its low energy consumption, CMOS compatibility, and high density …

Oscillatory Neural Networks (ONNs) are currently arousing interest in the research
community for their potential to implement very fast, ultra-low-power computing tasks by …

Graph neural network (GNN) is a variant of deep neural networks (DNNs) operating on
graphs. However, GNNs are more complex compared with DNNs as they simultaneously …

Resistive random access memory (RRAM) is an emerging non-volatile memory technology
that can be used in neuromorphic computing hardware to exceed the limitations of …

Memristor-based memory technology is one of the emerging memory technologies, which is
a potential candidate to replace traditional memories. Efficient test solutions are required to …

This work reports on the hardware implementation of analog dot-product operation on arrays
of two-dimensional (2D) hexagonal boron nitride (h-BN) memristors. This extends beyond …

Resistive random-access memory (ReRAM)-based manycore architectures enable
acceleration of graph neural network (GNN) inference and training. GNNs exhibit …

The growing popularity of convolutional neural networks (CNNs) has led to the search for
efficient computational platforms to accelerate CNN training. Resistive random-access …

Hardware-level reliability is a major concern when deep neural network (DNN) models are
mapped to neuromorphic accelerators such as memristor-based crossbars. Manufacturing …

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning
paradigm by encoding information into spikes. The discovery of the memristor has …