We review different technologies and architectures for neuromorphic photonic accelerators,
spanning from bulk optics to photonic-integrated-circuits (PICs), and assess compute …

The relentless growth of Artificial Intelligence (AI) workloads has fueled the drive towards
non-Von Neuman architectures and custom computing hardware. Neuromorphic photonic …

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 …

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 …

The computationally demanding nature of Deep Learning (DL) has fueled the research on
neuromorphics due to their potential to provide high-speed and low energy hardware …

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 …

Deep Learning (DL) has been extensively used in challenging tasks including security
applications such as Distributed Denial of Service (DDoS) attacks. However, the high speed …

Abstract Even nowadays, where Deep Learning (DL) has achieved state-of-the-art
performance in a wide range of research domains, accelerating training and building robust …