Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Ampere-Maxwell's Law: Problem-Solving

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Disrupting the epithelial-effector axis in type 2 inflammation: the strategic superiority of dual TSLP/IL-13 blockade in achieving immunological reset.

Journal of translational medicine·2026

Same author

Pixel-Level RGBT Fusion Tracking via Heterogeneous Multi-Expert Distillation and Decoupled Representation Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Inverse design of multi-point gain-clamped C + L-band discrete Raman amplifiers using a physics-informed neural network.

Optics express·2026

Same author

Accurate and generalized channel emulator of a hollow-core fiber communication system combining amplitude and phase.

Optics express·2026

Same author

Low-complexity mitigation of multipath interference in PAM-4 IM/DD systems via recursive filtering.

Optics express·2026

Same author

Spaceborne snapshot compressive hyperspectral imaging.

Light, science & applications·2026

Same journal

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor.

Optics express·2026

Same journal

Robust 3D topography measurement of large-range high-aspect-ratio structures based on dual-domain statistical filtering in SD-OCT.

Optics express·2026

Same journal

Broadband transmissive terahertz metasurface for simultaneous quad-mode OAM multiplexing.

Optics express·2026

Same journal

Leveraging two-dimensional materials for high-sensitivity optical sensors: quasi-bound states in the continuum within hybrid metasurfaces.

Optics express·2026

Same journal

Resolution investigation for dual-spherical-wave optical scanning holographic microscopy: methods and performance.

Optics express·2026

Same journal

Robustness of parallel subnetwork-filtered diffractive deep neural networks.

Optics express·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 30, 2025

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

AWGR-based all-optical switching network for distributed machine learning.

Yuanzhi Guo, Xuwei Xue, Bingli Guo

|January 29, 2025

Summary

This summary is machine-generated.

We developed an all-optical switching network to speed up distributed machine learning (DML) training communication. This optical network accelerates DML training and improves cost and power efficiency compared to traditional electrical networks.

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Related Experiment Videos

Last Updated: May 30, 2025

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Area of Science:

Computer Science
Electrical Engineering
Machine Learning

Background:

The increasing size of training datasets and models in distributed machine learning (DML) has shifted the primary bottleneck from computation to communication.
Existing electrical switching networks struggle to meet the growing communication demands of large-scale DML.

Purpose of the Study:

To propose and evaluate an all-optical switching network architecture designed to accelerate the communication phase of DML training.
To demonstrate the performance, cost, and power efficiency benefits of optical switching for DML.

Main Methods:

Implementation of an all-optical switching network architecture.
Experimental validation of server-to-server communication latency and error rates under high traffic load (0.9).
Deployment of small-scale DML training experiments (Resnet50, Resnet101, Vgg19) on the proposed architecture and comparison with electrical switching networks.

Main Results:

Achieved error-free packet transmission with a low server-to-server latency of 385 ns at a traffic load of 0.9.
Demonstrated acceleration of DML training for Resnet50, Resnet101, and Vgg19 by 1.16x to 1.48x compared to electrical switching.
Showcased a 58.9% enhancement in cost efficiency and a 60.9% improvement in power efficiency over a 3-tier fat-tree electrical architecture.

Conclusions:

The proposed all-optical switching network architecture effectively addresses the communication bottleneck in DML training.
Optical switching offers significant advantages in terms of speed, cost, and power efficiency for large-scale machine learning.
This technology paves the way for more efficient and scalable distributed machine learning systems.