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.
This Technology Insight will demonstrate how to optimize data analytics and machine learning workloads for Intel® Architecture based data center platforms. Speaker: Pradeep Dubey Intel Fellow, Intel Labs Director, Parallel Computing Lab, Intel Corporation
While there are many different programming models for the Intel® Xeon Phi™ coprocessor (code-named Knights Corner (KNC)), this paper lists the more prevalent KNC programming models and further discusses some of the necessary changes to port and optimize KNC models for the Intel® Xeon Phi™ processor x200 self-boot platform.
This year marks the tenth anniversary of Intel® Threading Building Blocks (Intel® TBB).
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.
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.
Tencent* Uses Machine Learning for In-Game Purchase Recommendation System on Intel® Xeon® ProcessorsTo enhance the online gaming user experience, Tencent uses an in-game purchase recommendation system employing the machine learning method to help users decide what equipment they would want to buy within their games. Tencent machine learning engine uses DGEMM6 extensively in its module to compute the coefficients for the logistic regression machine learning algorithm.