The recent explosive compute growth, mainly fueled by the boost of artificial intelligence (AI)
and deep neural networks (DNNs), is currently instigating the demand for a novel computing …

Abstract Recent advances in Deep Learning (DL) fueled the interest in developing
neuromorphic hardware accelerators that can improve the computational speed and energy …

The field of neuromorphic photonics has been projected to comprise the next-generation
Neural Network platform, expected to lead to remarkable advances in compute energy-and …

Neuromorphic photonic accelerators for Deep Learning (DL) have increasingly gained
attention over the recent years due to their ability for ultra fast matrix-based calculations and …

Photonic neural network accelerators (PNNAs) have been lately brought into the spotlight as
a new class of custom hardware that can leverage the maturity of photonic integration …

The energy demanding nature of deep learning (DL) has fueled the immense attention for
neuromorphic architectures due to their ability to operate in a very high frequencies in a very …

While the use of graphic processing units fueled the success of artificial intelligence models,
their future evolution will likely require overcoming the speed and energy efficiency …

Deep Learning (DL) has dominated a wide range of applications due to its state-of-the-art
performance. Novel approaches introduce Artificial Neural Networks (ANNs) on fiber …

The recent advances of Deep Learning (DL) have fueled the research of hardware
accelerators, which can signif-icantly improve the computation speed and energy efficiency …

Neuromorphic photonic accelerators are becoming increasingly popular, since they can
significantly improve computation speed and energy efficiency, leading to femtojoule per …