In the 1800s, Charles Babbage envisioned computers as analog devices. However, it was
not until 150 years later that a Mechanical Analog Computer was constructed for the US …

Recent work on in-network machine learning (ML) anticipates offline models to operate well
in modern networking environments. However, upon deployment, these models struggle to …

Abstract Machine learning studies need colossal power to process massive datasets and
train neural networks to reach high accuracies, which have become gradually …

Photonics is a promising technology to accelerate Deep Neural Networks as it can use
optical interconnects to reduce data movement energy and it enables low-energy, high …

The Ising machine (IM) has emerged as a promising tool for tackling nondeterministic
polynomial-time hard combinatorial optimization problems in real-world applications. Among …

We demonstrate Lightning, a reconfigurable photonic-electronic deep learning smartNIC
that serves real-time inference requests at 4.055 GHz compute frequency. To do so …

Targeting vision applications at the edge, in this work, we systematically explore and
propose a high-performance and energy-efficient Optical In-Sensor Accelerator architecture …

Integrated photonic neural networks (PNNs) usually adopt traditional convolutional neural
network (CNN) or multilayer perceptron (MLP) network models. These models consist of …

A new architectural paradigm, named, optical-computing-enabled network, is proposed as a
potential evolution of the currently used optical-bypass framework. The main idea is to …

We consider a mathematical model of a planar Mach–Zehnder interferometer with nonideal
beam splitters. For this model, we obtain two fidelity estimates of the matrix–vector …