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.
Autor Gennady F. (Blackbelt) Última actualización 05/07/2019 - 14:54

Caffe* Scoring Optimization for Intel® Xeon® Processor E5 Series

    In continued efforts to optimize Deep Learning workloads on Intel® architecture, our engineers explore various paths leading to the maximum performance.

Autor Gennady F. (Blackbelt) Última actualización 21/03/2019 - 12:28

BigDL: Distributed Deep Learning on Apache Spark*

As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a wide range of data analysis tasks—including data wrangling, interactive queries, and stream processing—within a single framework. Three important features offered by BigDL are rich deep learning support, High Single Node Xeon...
Autor Última actualización 11/03/2019 - 13:17

BigDL – Scale-out Deep Learning on Apache Spark* Cluster

Learn how to install and use BigDL for training and testing some of the commonly used deep neural network models on Apache Spark.
Autor Sunny G. (Intel) Última actualización 11/03/2019 - 13:17

Training an Agent to Play Pong* Using neon™ Framework

This article showcases the implementation of an agent to play the game Pong* using an Intel® architecture-optimized neon™ framework, and to serve as an introduction to the Policy Gradients algorithm.
Autor Kaustav Tamuly Última actualización 18/06/2018 - 16:10

Binary Neural Networks

In this article we delve into the theory, training procedure, and performance of binary neural networks (BNNs).
Autor Yash Akhauri Última actualización 08/05/2019 - 15:30

Code Sample: Optimizing Binarized Neural Networks on Intel® Xeon® Scalable Processors

In the previous article, we discussed the performance and accuracy of Binarized Neural Networks (BNN). We also introduced a BNN coded from scratch in the Wolfram Language. The key component of this neural network is Matrix Multiplication.
Autor Yash Akhauri Última actualización 21/03/2019 - 12:40

TensorFlow* Sample Codes for Distributed Image Classification

The TensorFlow* image classification sample codes below describe a step-by-step approach to modify the code in order to scale the deep learning training across multiple nodes of HPC data centers.

Autor Michael Steyer (Intel) Última actualización 11/03/2019 - 13:17

Code Sample: Intel® AVX512-Deep Learning Boost: Intrinsic Functions

How developers can use to take advantage of the new Intel® AVX512-Deep Learning Boost (Intel® AVX512-DL Boost) instructions.
Autor Alberto V. (Intel) Última actualización 02/04/2019 - 10:04