• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Nvidia debuts 2 Pascal-based Tesla chips for deep learning applications

September 14, 2016   Big Data

Nvidia wants the artificial intelligence and deep learning markets badly. It launched its 15-billion transistor Tesla P100 chip in April for deep-learning applications. And now it is announcing two more deep-learning Tesla chips today at an event in China.

Jen-Hsun Huang, CEO of Nvidia, announced the Tesla P4 and Tesla P40 graphics processing units (GPU) at the GPU Technology conference in Beijing. He also announced the company’s TensorRT and DeepStream software to boost A.I. for video inferencing as well. The announcements show that A.I. and deep learning are driving the high end of chip development like never before. They will enable A.I.-based services such as voice-activated assistance, email spam filters, and movie and product recommendation engines.

The new chips are based on the Nvidia Pascal architecture, which started debuting for consumer and business markets this spring. Ian Buck, general manager of accelerated computing at Nvidia, said in an interview with VentureBeat that the new chips give customers several options for how they attack deep learning processing challenges. He said these chips deliver “massive leaps in efficiency and speed.”

Deep learning tasks are divided into training, where a deep learning neural network is trained to recognize patterns, and inferencing, where the trained neural network actually identifies images.

The Tesla P100 focuses on training tasks. But the 7.2 billion-transistor Tesla P4 (with 2,560 CUDA cores) and the 12-billion transistor Tesla P40 (3,840 CUDA cores) are designed to recognize speech, images, or text in response to queries from users and devices.

The market is changing fast. Buck said that neural networks require 10 times more computing power than just a year ago. And he said that current central processing unit (CPU) technology isn’t capable of keeping up in real-time responsiveness for A.I. Intel, of course, disagrees, and it notes that GPU computing is only 3 percent of the current server market. Intel has been beefing up its own A.I. computing technology with a couple of acquisitions, and it is working on a new version of its A.I.-focused Xeon Phi CPUs. Nvidia said its response time is 45 times faster than CPU solutions such as Intel’s latest Broadwell chip, and its new Tesla chips are four times more powerful than graphics chips that came out last year.

“A.I. is everywhere these days,” Buck said. “Google said that one in five searches are now initiated by voice. A.I. is in everything from Skype [having] automatic translation in real time to predicting what treatment babies need in hospitals. It can enable the blind to use sensors that detect the emotions on the faces of people around them, or it can help find a lost child at the mall.”

tesla p40 800x412 Nvidia debuts 2 Pascal based Tesla chips for deep learning applications

Above: Nvidia Tesla P40 graphics card for deep learning inference applications.

Image Credit: Nvidia

Buck said the Tesla P4 delivers the highest energy efficiency for hyperscale data centers. It fits in any server with its small form factor, and its low-power design, which starts at 50 watts, helps to make it 40 times more energy efficient than CPUs for inferencing workloads. A server with a single Tesla P4 replaces 13 CPU servers for video inferencing workloads, Nvidia said. It can deliver eight times savings in total cost of ownership, Nvidia said. The P40 uses 250 watts.

Meanwhile, the Tesla P40 delivers maximum throughput for deep learning workloads. With 47 tera-operations per second (TOPS) of inference performance with INT8 instructions, a server with eight Tesla P40 accelerators can replace the performance of more than 140 CPU servers, Nvidia said, At approximately $ 5,000 per server, this results in savings of more than $ 650,000 in server acquisition cost, Nvidia said. And it can slash training time from hours to days.

“The P4 fits in any servers and delivers higher efficiency and performance on 50 or 75 watts of power,” Buck said. “The P40 has the highest-performance throughput for scale-up servers.”

Complementing the Tesla P4 and P40 are two software innovations to accelerate AI inferencing: the Nvidia TensorRT and the Nvidia DeepStream software development kit (SDK).

TensorRT is a library created for optimizing deep learning models for production deployment that delivers instant responsiveness for the most complex networks. It maximizes throughput and efficiency of deep learning applications by taking trained neural nets — usually in 32-bit or 16-bit data — and optimizing them for reduced-precision INT8 operations.

The DeepStream SDK taps into the power of a Pascal server to simultaneously decode and analyze up to 93 HD video streams in real time, compared to seven streams with dual CPUs. That can help tackle one of the grand challenges of A.I.: understanding huge amounts of video content for applications such as self-driving cars, interactive robots, filtering, and ad placement.

“Delivering simple and responsive experiences to each of our users is very important to us,” said Greg Diamos, senior researcher at Baidu, in a statement. “We have deployed Nvidia GPUs in production to provide AI-powered services such as our Deep Speech 2 system and the use of GPUs enables a level of responsiveness that would not be possible on un-accelerated servers. Pascal with its INT8 capabilities will provide an even bigger leap forward and we look forward to delivering even better experiences to our users.”

The Tesla P4 will come out in November, and Tesla P40 will be out in October. Nvidia’s partners include Dell, Hewlett Packard, Inspur, Inventec, and Lenovo.

Get more stories like this on Twitter & Facebook

Nvidia specializes in the manufacture of graphics-processor technologies for workstations, desktop computers, and mobile devices. The company, based in Santa Clara, California, is a major su… All Nvidia Corporation news »

VB Profile Logo Nvidia debuts 2 Pascal based Tesla chips for deep learning applicationsTrack Nvidia Corporation’s Landscape to stay on top of the industry. Access the entire ecosystem, track innovation & deals. Learn more.

Let’s block ads! (Why?)

Big Data – VentureBeat

applications, Chips, Debuts, deep, Learning, Nvidia, Pascalbased, “Tesla
  • Recent Posts

    • P3 Jobs: Time to Come Home?
    • NOW, THIS IS WHAT I CALL AVANTE-GARDE!
    • Why the open banking movement is gaining momentum (VB Live)
    • OUR MAGNIFICENT UNIVERSE
    • What to Avoid When Creating an Intranet
  • Categories

  • Archives

    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited