Neuromorphic computing uses brain-inspired principles to design circuits that can perform
computational tasks with superior power efficiency to conventional computers. Approaches …

Modern computing schemes require large circuit areas and large energy consumption for
neuromorphic computing applications, such as recognition, classification, and prediction …

We study the timescale of random telegraph noise (RTN) of nanomagnets in stochastic
magnetic tunnel junctions (MTJs). From analytical and numerical calculations based on the …

We introduce the concept of a probabilistic or p-bit, intermediate between the standard bits
of digital electronics and the emerging q-bits of quantum computing. We show that low …

Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS)
transistors with emerging nanotechnologies (X) has become increasingly important. One …

Spintronic devices have recently attracted a lot of attention in the field of unconventional
computing due to their non-volatility for short-and long-term memory, nonlinear fast …

This work investigates nanosecond superparamagnetic switching in 50-nm-diameter in-
plane magnetized magnetic tunnel junctions (MTJs). Due to the small in-plane uniaxial …

True random number generators are of great interest in many computing applications, such
as cryptography, neuromorphic systems, and Monte Carlo simulations. Here, we investigate …

Binary stochastic neurons (BSNs) form an integral part of many machine learning
algorithms, motivating the development of hardware accelerators for this complex function. It …

Magnetic skyrmions, which are a topologically nontrivial spin texture, have been considered
as promising information carriers in future electronic devices because of their nanoscale …