Artificial intelligence (AI) is gaining strength, and materials science can both contribute to
and profit from it. In a simultaneous progress race, new materials, systems, and processes …

Photonic realizations of neural network computing hardware are a promising approach to
enable future scalability of neuromorphic computing. The number of special purpose …

Reservoir computing (RC) is a computational architecture capable of efficiently processing
temporal information, which allows low-cost hardware implementation. However, the …

We report on the first numerical implementation of photonic reservoir computing (RC) based
on an optically pumped spin vertical-cavity surface-emitting laser (spin VCSEL) with optical …

Deep neural networks usually process information through multiple hidden layers. However,
most hardware reservoir computing recurrent networks only have one hidden reservoir …

Vertical-Cavity Surface-Emitting Lasers (VCSELs) are highly promising devices for the
construction of neuromorphic photonic information processing systems, due to their …

A semiconductor laser with optical feedback and optical injection is an appealing scheme to
construct the time-delay reservoir computing (TDRC) networks. Quantum dot (QD) lasers are …

Reservoir computing is a simplified recurrent neural network, which requires training only at
the output layer and hence significantly reduces the training cost. Time-delay reservoir …

Time-delay reservoir computing uses a nonlinear node associated with a feedback loop to
construct a large number of virtual neurons in the neural network. The clock cycle of the …

We propose and numerically demonstrate two reservoir computing (RC) schemes using
multiple vertical-cavity surface-emitting lasers (VCSELs) with different external injections, ie …