Spintronics is a growing research field that focuses on exploring materials and devices that
take advantage of the electron's “spin” to go beyond charge based devices. The most …

The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) makes
SNNs promising for edge applications that require high energy efficiency. To realize SNNs …

Bayesian neural networks (BNNs) combine the generalizability of deep neural networks
(DNNs) with a rigorous quantification of predictive uncertainty, which mitigates overfitting …

Topological solitons are exciting candidates for the physical implementation of next-
generation computing systems. As these solitons are nanoscale and can be controlled with …

Neuromorphic computing with spintronic devices has been of interest due to the limitations
of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) …

Brain-inspired neuromorphic computing is a promising path towards next generation
analogue computers that are fundamentally different compared to the conventional von …

The impressive performance of artificial neural networks has come at the cost of high energy
usage and CO 2 emissions. Unconventional computing architectures, with magnetic systems …

Domain wall (DW) devices have emerged as promising candidates for ultrafast and low
power spintronic computing systems. However, the hardware implementation of these …

Neuromorphic computing, commonly understood as a computing approach built upon
neurons, synapses, and their dynamics, as opposed to Boolean gates, is gaining large …

Neuromorphic computing (NC) is considered a potential solution for energy-efficient artificial
intelligence applications. The development of reliable neural network (NN) hardware with …