Improving the computational performance of a deep learning framework
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. This version of Caffe, known as Caffe optimized for Intel architecture, is currently integrated with the latest release of Intel® Math Kernel Library 2017 and is optimized for Intel® Advanced Vector Extensions 2 and will include Intel Advanced Vector Extensions 512 instructions. This solution is supported by Intel® Xeon® processors and Intel® Xeon Phi™ processors, among others. This paper includes performance results for a CIFAR-10* image-classification dataset, and it describes the tools and code modifications that can be used to improve computational performance for the BVLC Caffe code and other deep learning frameworks.