The simultaneous advances in artificial neural networks and photonic integration
technologies have spurred extensive research in optical computing and optical neural …

The explosive growth of deep learning applications has triggered a new era in computing
hardware, targeting the efficient deployment of multiply-and-accumulate operations. In this …

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 …

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 …

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

Non-von-Neumann computing architectures and deep learning training models have
sparked a new computational era where neurons are forming the main architectural …

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