In recent years, the explosive development of artificial intelligence implementing by artificial
neural networks (ANNs) creates inconceivable demands for computing hardware. However …

The concept of reservoir computing emerged from a specific machine learning paradigm
characterized by a three-layered architecture (input, reservoir, and output), where only the …

Recent advances in neuromorphic computing have established a computational framework
that removes the processor-memory bottleneck evident in traditional von Neumann …

Photonic implementations of reservoir computing (RC) promise to reach ultra-high
bandwidth of operation with moderate training efforts. Several optoelectronic demonstrations …

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 …

With the rapid development of optical communication systems, more advanced techniques
conventionally used in long-haul transmissions have gradually entered systems covering …

The cloud edge data center will enable reliable and low latency options for the network, and
the interconnection among these data-centers will demand a scalable low-complexity …

We propose a scalable photonic architecture for implementation of feedforward and
recurrent neural networks to perform the classification of handwritten digits from the MNIST …