Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …

The explosive growth of deep learning applications has triggered a new era in computing
hardware, targeting the efficient deployment of multiply-and-accumulate operations. In this …

Machine learning (ML), a subset of artificial intelligence, refers to methods that have the
ability to “learn” from experience, enabling them to carry out designated tasks. Examples of …

Advances in deep learning research over the past decade have been enabled by an
increasingly unsustainable demand for compute power. This trend has dramatically …

Deep neural networks unlocked a vast range of new applications by solving tasks of which
many were previously deemed as reserved to higher human intelligence. One of the …

Artificial neural networks have become a staple computing technique in many fields. Yet,
they present fundamental differences with classical computing hardware in the way they …

A reservoir computer is a type of dynamical system arranged to do computation. Typically, a
reservoir computer is constructed by connecting a large number of nonlinear nodes in a …

Due to challenging efficiency limits facing conventional and unconventional electronic
architectures, information processors based on photonics have attracted renewed interest …

This paper investigates in detail the effects of measurement noise on the performance of
reservoir computing. We focus on an application in which reservoir computers are used to …

We show that multiplexing (Here, the term “multiplexing” means a special network topology
where a one-layer network is connected to another one-layer networks through coupling …