Ising machines, which are hardware implementations of the Ising model of coupled spins,
have been influential in the development of unsupervised learning algorithms at the origins …

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

The convergence of spintronics and traditional semiconductor technology marks a critical
juncture in the evolution of computing architectures, for which the development of foundation …

Networks of coupled oscillators have far-reaching implications across various fields,
providing insights into a plethora of dynamics. This review offers an in-depth overview of …

Optical computing often employs tailor-made hardware to implement specific algorithms,
trading generality for improved performance in key aspects like speed and power efficiency …

Inspired by many-body effects, we propose a novel design for Boltzmann machine (BM)-
based invertible logic (IL) using probabilistic bits (p-bits). A CMOS-based XNOR gate is …

Artificial intelligence has surged forward with the advent of generative models, which rely
heavily on stochastic computing architectures enhanced by true random number generators …

Magnetic tunnel junction (MTJ)-based true random number generators (TRNG), which are
promisingly utilized as hardware accelerators for probabilistic computing, may suffer intrinsic …

Ising machines are hardware accelerators for combinatorial optimization and probabilistic
sampling, using stochasticity to explore spin configurations and avoid local minima. We …

Physical neural networks (PNNs) are a class of neural-like networks that leverage the
properties of physical systems to perform computation. While PNNs are so far a niche …