Tuesday, August 21, 2018
Search
  
Submit your own News for
inclusion in our Site.
Click here...
Breaking News
Samsung Launches CJG5 Curved Gaming Monitor
Samsung Extends Semiconductor Market Lead in 1H18
Apple Is Planning a New Low-Cost MacBook and New Mac Mini
HTC VIVE Debuts the Vive Wireless Adapter
HP kicks off Gamescom with OMEN Products
New TONE Platinum SE Offers Google Translate and Other AI Features
New Alienware Gaming Desktops and Dell Gaming Monitors Announced at Gamescom
NVIDIA Brings Real-Time Ray Tracing to Gamers with GeForce RTX Graphics Card
Active Discussions
Which of these DVD media are the best, most durable?
How to back up a PS2 DL game
Copy a protected DVD?
roxio issues with xp pro
Help make DVDInfoPro better with dvdinfomantis!!!
menu making
Optiarc AD-7260S review
cdrw trouble
 Home > News > General Computing > Intel O...
Last 7 Days News : SU MO TU WE TH FR SA All News

Thursday, April 19, 2018
Intel Open Sources the nGraph Compiler for Deep Learning Systems


Intel's nGraph Compiler, a framework-neutral deep neural network (DNN) model compiler, is now open source, allowing support for multiple deep learning frameworks while optimizing models for multiple hardware solutions.

"Finding the right technology for AI solutions can be daunting for companies, and it's our goal to make it as easy as possible. With the nGraph Compiler, data scientists can create deep learning models without having to think about how that model needs to be adjusted across different frameworks, and its open source nature means getting access to the tools they need, quickly and easily," said Arjun Bansal, VP, AI Software, Intel.

With nGraph, data scientists can focus on data science rather than worrying about how to adapt their DNN models to train and run efficiently on different devices.

Currently, the nGraph Compiler supports three deep learning compute devices and six third-party deep learning frameworks: TensorFlow, MXNet, neon, PyTorch, CNTK and Caffe2. Users can run these frameworks on several devices: Intel Architecture (x86, Intel Xeon and Xeon Phi), GPU (NVIDIA cuDNN), and Intel Nervana Neural Network Processor (NNP).

When Deep Learning (DL) frameworks first emerged as the vehicle for running training and inference models, they were designed around kernels optimized for a particular device. As a result, many device details were being exposed in the model definitions, complicating the adaptability and portability of DL models to other, or more advanced, devices.

The traditional approach means that an algorithm developer faces tediousness in taking their model to an upgraded device. Enabling a model to run on a different framework is also problematic because the developer must separate the essence of the model from the performance adjustments made for the device, translate to similar ops in the new framework, and finally make the necessary changes for the preferred device configuration on the new framework.

Intel designed the nGraph library to reduce these kinds of engineering complexities. While optimized kernels for DL primitives are provided through the project and via libraries like Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN), there are also several compiler-inspired ways in which performance can be further optimized.



Previous
Next
Garmin Announces Connect IQ 3.0 with New apps from Trailforks, Yelp, iHeartRadio        All News        Facebook Seeking to Hire Chip Designers
SpaceX Successfully Launches NASA's TESS Spacecraft     General Computing News      Facebook Seeking to Hire Chip Designers

Get RSS feed Easy Print E-Mail this Message

Related News
Samsung Extends Semiconductor Market Lead in 1H18
Intel Introduces New NUC Kits and NUC Mini PCs
Intel Discloses New Chip Security Flaws
Intel Sets the Stage for the Persistent Memory Revolution and FPGA Acceleration
Intel Promotes Optane And QLC Solutions, World's Densest 'Ruler' SSD
Intel Outlines 'Data-Centric' Strategy, Shows off Xeon Roadmap
Intel Intros 660p Series M.2 NVMe SSDs with QLC NAND Flash
Broadcom to Design 7-nm AI processor For Wave: report
Intel Claims Progress on 10nm Yields But Pushes back Release Schedule
DARPA to Work With Nvidia, Qualcomm, IBM and Intel on Post-Moore's-law Projects
Micron and Intel to End Their 3D XPoint Joint Development Partnership
Microsoft Calls for Public Regulation of AI Face Recognition Software

Most Popular News
 
Home | News | All News | Reviews | Articles | Guides | Download | Expert Area | Forum | Site Info
Site best viewed at 1024x768+ - CDRINFO.COM 1998-2018 - All rights reserved -
Privacy policy - Contact Us .