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

Photonic delay systems have revolutionized the hardware implementation of Recurrent
Neural Networks and Reservoir Computing in particular. The fundamental principles of …

Photonic neuromorphic computing is of particular interest due to its significant potential for
ultrahigh computing speed and energy efficiency. The advantage of photonic computing …

In the emerging Internet of things cyber-physical system-embedded society, big data
analytics needs huge computing capability with better energy efficiency. Coming to the end …

Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …

Deep learning has been revolutionizing information processing in many fields of science
and engineering owing to the massively growing amounts of data and the advances in deep …

We propose a scheme for reservoir computing using multiple semiconductor lasers with
optical feedback arranged in parallel on a photonic integrated circuit, and we investigate the …

Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed
in no small part by the rise of deep learning in many applications. In this paper, we will …

Neuromorphic computing using photonic hardware is a promising route towards ultrafast
processing while maintaining low power consumption. Here we present and numerically …

Nonlinearity mitigation in optical fiber networks is typically handled by electronic Digital
Signal Processing (DSP) chips. Such DSP chips are costly, power-hungry and can introduce …