Research & Development World

  • R&D World Home
  • Topics
    • Aerospace
    • Automotive
    • Biotech
    • Careers
    • Chemistry
    • Environment
    • Energy
    • Life Science
    • Material Science
    • R&D Management
    • Physics
  • Technology
    • 3D Printing
    • A.I./Robotics
    • Software
    • Battery Technology
    • Controlled Environments
      • Cleanrooms
      • Graphene
      • Lasers
      • Regulations/Standards
      • Sensors
    • Imaging
    • Nanotechnology
    • Scientific Computing
      • Big Data
      • HPC/Supercomputing
      • Informatics
      • Security
    • Semiconductors
  • R&D Market Pulse
  • R&D 100
    • Call for Nominations: The 2025 R&D 100 Awards
    • R&D 100 Awards Event
    • R&D 100 Submissions
    • Winner Archive
    • Explore the 2024 R&D 100 award winners and finalists
  • Resources
    • Research Reports
    • Digital Issues
    • R&D Index
    • Subscribe
    • Video
    • Webinars
  • Global Funding Forecast
  • Top Labs
  • Advertise
  • SUBSCRIBE

NYU to Advance Deep Learning Research with Multi-GPU Cluster

By R&D Editors | May 5, 2015

NYU has installed a new computing system for next-generation deep learning research — called “ScaLeNet” — that will allow researchers to significantly increase the size of deep learning models that can be trained and number of models that can be tested. Self-driving cars. Computers that detect tumors. Real-time speech translation.

Just a few years ago, deep learning — training computers to identify patterns and objects, much like the way humans do — was the domain of a few artificial intelligence and data science researchers. No longer.

Today, top experts use it to do amazing things. And they continue to push the bounds of what’s possible.

That’s why New York University’s Center for Data Science and NVIDIA are teaming up to develop next-gen deep learning applications and algorithms for large-scale GPU-accelerated systems.

Founded by deep learning pioneer Yann LeCun, who’s also director of AI Research at Facebook, NYU’s Center for Data Science (CDS) is one of several top institutions NVIDIA works with to push GPU-based deep learning forward.

Pushing the Deep Learning Technology Envelope

Tomorrow’s advances in deep learning rely on new, more sophisticated algorithms. They’re designed to help computers achieve — even surpass — human capabilities.

They also require the latest, most advanced computing technologies.

This is where GPU technology comes in. GPUs are the go-to technology for deep learning, reducing the time it takes to train neural networks by days, even months.

But, until now, many researchers worked on systems with only one GPU. This limits the number of training parameters and the size of the models researchers can develop.

By distributing the deep learning training process among many GPUs, researchers can increase the size of the models that can be trained and the number of models that can be tested. The result: more accurate models and new classes of applications.

Recognizing this, NYU recently installed a new deep learning computing system — called “ScaLeNet.” It’s an eight-node Cirrascale cluster with 64 top-of-the-line NVIDIA Tesla K80 dual-GPU accelerators.

The new high-performance system will let NYU researchers take on bigger challenges, and create deep learning models that let computers do human-like perceptual tasks.

“Multi-GPU machines are a necessary tool for future progress in AI and deep learning. Potential applications include self-driving cars, medical image analysis systems, real-time speech-to-speech translation, and systems that can truly understand natural language and hold dialogs with people,” says LeCun.

ScaLeNet will be used for research projects and educational programs at CDS by a large community of faculty members, research scientists, postdoctoral fellows and graduate students.

So, expect big things.

“CDS has research projects that apply machine and deep learning to the physical, life and social sciences,” LeCun says. “This includes Bayesian models of cosmology and high-energy physics, computational models of the visual and motor cortex, deep learning systems for medical and biological image analysis, as well as machine-learning models of social behavior and economics.”

LeCun hopes the work at NYU can serve as a model used to advance the field of deep learning and train the next generation of AI experts.

LeCun and some of his Facebook and NYU colleagues will present a paper in May at the International Conference on Learning Representations in San Diego. The paper discusses a fast, multi-GPU implementation of convolutional networks — a type of deep learning model used for image and video understanding. To learn more or to register, visit the conference Web site.

Related Articles Read More >

Why IBM predicts quantum advantage within two years
Aardvark AI forecasts rival supercomputer simulations while using over 99.9% less compute
This week in AI research: Latest Insilico Medicine drug enters the clinic, a $0.55/M token model R1 rivals OpenAI’s $60 flagship, and more
How the startup ALAFIA Supercomputers is deploying on-prem AI for medical research and clinical care
rd newsletter
EXPAND YOUR KNOWLEDGE AND STAY CONNECTED
Get the latest info on technologies, trends, and strategies in Research & Development.
RD 25 Power Index

R&D World Digital Issues

Fall 2024 issue

Browse the most current issue of R&D World and back issues in an easy to use high quality format. Clip, share and download with the leading R&D magazine today.

Research & Development World
  • Subscribe to R&D World Magazine
  • Enews Sign Up
  • Contact Us
  • About Us
  • Drug Discovery & Development
  • Pharmaceutical Processing
  • Global Funding Forecast

Copyright © 2025 WTWH Media LLC. All Rights Reserved. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of WTWH Media
Privacy Policy | Advertising | About Us

Search R&D World

  • R&D World Home
  • Topics
    • Aerospace
    • Automotive
    • Biotech
    • Careers
    • Chemistry
    • Environment
    • Energy
    • Life Science
    • Material Science
    • R&D Management
    • Physics
  • Technology
    • 3D Printing
    • A.I./Robotics
    • Software
    • Battery Technology
    • Controlled Environments
      • Cleanrooms
      • Graphene
      • Lasers
      • Regulations/Standards
      • Sensors
    • Imaging
    • Nanotechnology
    • Scientific Computing
      • Big Data
      • HPC/Supercomputing
      • Informatics
      • Security
    • Semiconductors
  • R&D Market Pulse
  • R&D 100
    • Call for Nominations: The 2025 R&D 100 Awards
    • R&D 100 Awards Event
    • R&D 100 Submissions
    • Winner Archive
    • Explore the 2024 R&D 100 award winners and finalists
  • Resources
    • Research Reports
    • Digital Issues
    • R&D Index
    • Subscribe
    • Video
    • Webinars
  • Global Funding Forecast
  • Top Labs
  • Advertise
  • SUBSCRIBE