Performance Optimization of Deep Learning Frameworks Caffe* and Tensorflow* for Xeon Phi Cluster

In this talk, we analyze the performance characteristics of Caffe* and TensorFlow* on Intel Xeon Phi processor x200. It is the latest processor using Intel Many Integrated Core Architecture (Intel MIC Architecture). It introduces several state-of-the-art features such as a compute core with two 512-bit vector processing units and an on-chip, high-bandwidth multichannel DRAM (MCDRAM) memory, delivering a theoretical peak performance of 6 TF single precision and 3 TF double precision floating point operations per second. We give an overview of the DNN framework architectures and describe the usage of Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) APIs in the implementation of different neural network layer computations. We present the details on the integration and performance optimizations of few of the compute intensive layers using Intel MKL-DNN APIs.