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 …

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 …

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 …

We demonstrate an integrated opto-electronic (ΟΕ) device that can be programmed to
provide a set of nonlinear activation functions (AFs) and present its operation within …

Using photons in lieu of electrons to process information has been an exciting technological
prospect for decades. Optical computing is gaining renewed enthusiasm, owing to the …

Visual adaptation is essential for optimizing the image quality and sensitivity of artificial
vision systems in real-world lighting conditions. However, additional modules, leading to …

This paper presents an experimental demonstration of the photonic segment of a photonic-
electronic multiply accumulate neuron (PEMAN) architecture, employing a silicon photonic …