Article

Building and Probing Prolog* with Intel® Architecture

This article explores what happens when Intel solutions support functional and logic programming languages that are regularly used for Artificial Intelligence (AI) and proposes a Prolog interpreter recompilation using Intel® C++ Compiler and libraries in order to evaluate their contribution to logic based AI.
Authored by Flavio Luis de Mello Last updated on 01/24/2018 - 15:35
Blog post

Track Reconstruction with Deep Learning at the CERN CMS Experiment

Connecting the Dots
Authored by Last updated on 12/12/2018 - 18:00
File Wrapper

Parallel Universe Magazine - Issue 28, April 2017

Authored by admin Last updated on 09/30/2019 - 16:45
Article

Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Authored by Last updated on 10/15/2019 - 15:30
Article

Scale-Up Implementation of a Transportation Network Using Ant Colony Optimization (ACO)

In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
Authored by Sunny G. (Intel) Last updated on 10/15/2019 - 16:40
Article

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
Authored by Gennady F. (Blackbelt) Last updated on 10/15/2019 - 16:50
Article

Accelerating Deep Learning Based Large-Scale Inverse Kinematics with Intel® Distribution of OpenVINO™ Toolkit

Use Deep Learning Deployment Toolkit (DLDT) to deploy deep-learning algorithms to solve character Inverse Kinematics (IK) problems.
Authored by Tai Ha (Intel) Last updated on 10/15/2019 - 21:09