• HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
Monday, January 18, 2021
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Chemistry

Light-carrying chips advance machine learning

Bioengineer by Bioengineer
January 6, 2021
in Chemistry
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

International team of researchers uses photonic networks for pattern recognition

IMAGE

Credit: WWU/AG Pernice

In the digital age, data traffic is growing at an exponential rate. The demands on computing power for applications in artificial intelligence such as pattern and speech recognition in particular, or for self-driving vehicles, often exceeds the capacities of conventional computer processors. Working together with an international team, researchers at the University of Münster are developing new approaches and process architectures which can cope with these tasks extremely efficient. They have now shown that so-called photonic processors, with which data is processed by means of light, can process information much more rapidly and in parallel – something electronic chips are incapable of doing. The results have been published in the “Nature” journal.

Background and methodology

Light-based processors for speeding up tasks in the field of machine learning enable complex mathematical tasks to be processed at enormously fast speeds (10¹² -10¹? operations per second). Conventional chips such as graphic cards or specialized hardware like Google’s TPU (Tensor Processing Unit) are based on electronic data transfer and are much slower. The team of researchers led by Prof. Wolfram Pernice from the Institute of Physics and the Center for Soft Nanoscience at the University of Münster implemented a hardware accelerator for so-called matrix multiplications, which represent the main processing load in the computation of neural networks. Neural networks are a series of algorithms which simulate the human brain. This is helpful, for example, for classifying objects in images and for speech recognition.

The researchers combined the photonic structures with phase-change materials (PCMs) as energy-efficient storage elements. PCMs are usually used with DVDs or BluRay discs in optical data storage. In the new processor this makes it possible to store and preserve the matrix elements without the need for an energy supply. To carry out matrix multiplications on multiple data sets in parallel, the Münster physicists used a chip-based frequency comb as a light source. A frequency comb provides a variety of optical wavelengths which are processed independently of one another in the same photonic chip. As a result, this enables highly parallel data processing by calculating on all wavelengths simultaneously – also known as wavelength multiplexing. “Our study is the first one to apply frequency combs in the field of artificially neural networks,” says Wolfram Pernice.

In the experiment the physicists used a so-called convolutional neural network for the recognition of handwritten numbers. These networks are a concept in the field of machine learning inspired by biological processes. They are used primarily in the processing of image or audio data, as they currently achieve the highest accuracies of classification. “The convolutional operation between input data and one or more filters – which can be a highlighting of edges in a photo, for example – can be transferred very well to our matrix architecture,” explains Johannes Feldmann, the lead author of the study. “Exploiting light for signal transference enables the processor to perform parallel data processing through wavelength multiplexing, which leads to a higher computing density and many matrix multiplications being carried out in just one timestep. In contrast to traditional electronics, which usually work in the low GHz range, optical modulation speeds can be achieved with speeds up to the 50 to 100 GHz range.” This means that the process permits data rates and computing densities, i.e. operations per area of processor, never previously attained.

The results have a wide range of applications. In the field of artificial intelligence, for example, more data can be processed simultaneously while saving energy. The use of larger neural networks allows more accurate, and hitherto unattainable, forecasts and more precise data analysis. For example, photonic processors support the evaluation of large quantities of data in medical diagnoses, for instance in high-resolution 3D data produced in special imaging methods. Further applications are in the fields of self-driving vehicles, which depend on fast, rapid evaluation of sensor data, and of IT infrastructures such as cloud computing which provide storage space, computing power or applications software.

###

Research partners

In addition to researchers at the University of Münster, scientists at the Universities of Oxford and Exeter in England, the University of Pittsburgh, USA, the École Polytechnique Fédérale (EPFL) in Lausanne, Switzerland, and the IBM research laboratory in Zurich were also involved in this work.

Media Contact
Prof Wolfram Pernice
[email protected]

Original Source

https://www.uni-muenster.de/news/view.php?cmdid=11463

Related Journal Article

http://dx.doi.org/10.1038/s41586-020-03070-1

Tags: Chemistry/Physics/Materials SciencesNanotechnology/Micromachines
Share12Tweet8Share2ShareShareShare2

Related Posts

IMAGE

Better diet and glucose uptake in the brain lead to longer life in fruit flies

January 16, 2021
IMAGE

Howard University professor to receive first Joseph A. Johnson Award

January 15, 2021

Nanodiamonds feel the heat

January 15, 2021

Controlling chemical catalysts with sculpted light

January 15, 2021
Next Post
IMAGE

A prognostic Alzheimer's disease blood test in the symptom-free stage

IMAGE

Citizenship tasks tax women physicians

Leave a Reply Cancel reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.

POPULAR NEWS

  • IMAGE

    The map of nuclear deformation takes the form of a mountain landscape

    53 shares
    Share 21 Tweet 13
  • Blood pressure drug may be key to increasing lifespan, new study shows

    44 shares
    Share 18 Tweet 11
  • New drug form may help treat osteoporosis, calcium-related disorders

    39 shares
    Share 16 Tweet 10
  • New findings help explain how COVID-19 overpowers the immune system

    35 shares
    Share 14 Tweet 9

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Tags

Infectious/Emerging DiseasesClimate ChangePublic HealthMedicine/HealthBiologyCell BiologycancerMaterialsGeneticsEcology/EnvironmentChemistry/Physics/Materials SciencesTechnology/Engineering/Computer Science

Recent Posts

  • Scientists shed light on how and why some people report “hearing the dead”
  • Changing diets — not less physical activity — may best explain childhood obesity crisis
  • Better diet and glucose uptake in the brain lead to longer life in fruit flies
  • Rapid blood test identifies COVID-19 patients at high risk of severe disease
  • Contact Us

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

Welcome Back!

Login to your account below

Forgotten Password?

Create New Account!

Fill the forms below to register

All fields are required. Log In

Retrieve your password

Please enter your username or email address to reset your password.

Log In