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.
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.
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.
How to optimize Caffe* for Intel® Architecture, train deep network models, and deploy networks.
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.
Please notes: Deep Neural Network(DNN) component in MKL is deprecated since intel® MKL 2019 and will be removed in the next intel® MKL Release.
Learn to build a face access control solution, get horrified in a haunted high school, and be sure to register for the Intel® HPC Developer Conference this month.
Create a safe VR social environment and learn more about object detection specifically for drone videos.
This document provides optimization tips for TensorFlow*, Keras, and Caffe* on Intel® Xeon® processors.