Analog photonic computing comprises a promising candidate for accelerating the linear
operations of deep neural networks (DNNs), since it provides ultrahigh bandwidth, low …

We demonstrate a programmable analog opto-electronic (OE) circuit that can be configured
to provide a range of nonlinear activation functions for incoherent neuromorphic photonic …

The explosive volume growth of deep-learning (DL) applications has triggered an era in
computing, with neuromorphic photonic platforms promising to merge ultra-high speed and …

Reprogrammable optical meshes comprise a subject of heightened interest for the execution
of linear transformations, having a significant impact in numerous applications that extend …

Recently there has been growing interest in using photonics to perform the linear algebra
operations of neuromorphic and quantum computing applications, aiming at harnessing …

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 photonics is a cutting-edge fusion of neuroscience-inspired computing and
photonics technology to overcome the constraints of conventional computing architectures …

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 …