Abstract Machine learning techniques are commonly used to model complex relationships
but implementations on digital hardware are relatively inefficient due to poor matching …

The biologist JBS Haldane, when asked what we can learn about the Creator by examining
the world, is said to have replied “an inordinate fondness for beetles”[705]. Today's …

Learning and pattern recognition inevitably requires memory of previous events, a feature
that conventional CMOS hardware needs to artificially simulate. Dynamical systems …

We numerically study reservoir computing on a spin-torque oscillator (STO) array, describing
the magnetization dynamics of the STO array by a nonlinear oscillator model. The STOs …

The quest to implement machine learning algorithms in hardware has focused on combining
various materials, each mimicking a computational primitive, to create device functionality …

Brain-inspired computing employs devices and architectures that emulate biological
functions for more adaptive and energy-efficient systems. Oscillatory neural networks …

In-memory computing represents an effective method for modeling complex physical
systems that are typically challenging for conventional computing architectures but has been …

In this work we present an in-memory computing platform based on coupled VO2 oscillators
fabricated in a crossbar configuration on silicon. Compared to existing platforms, the …

Spintronics is generally defined as a new type of electronics manipulating electrons by
acting not only on the charge of the electrons but also on their spin. Its development started …

We study injection locking of two different spin wave (SW) modes (a field-localized linearlike
interior mode and a self-localized SW bullet soliton) in a single nanoconstriction-based spin …