TensorFlow* is one of the leading deep learning and machine learning frameworks today. Earlier in 2017, Intel worked with Google to incorporate optimizations for Intel Xeon and Xeon Phi processor based platforms using Intel Math Kernel Libraries (Intel MKL). These optimizations resulted in orders of magnitude improvement in performance – up to 70x[1] higher performance for training and up to 85x[2] higher performance for inference. In this blog we provide a performance update for a number of deep learning models running on the Intel Xeon Scalable processor. The Intel Xeon Scalable processor provides up to 28 cores, which brings additional computing power to the table compared to the 22 cores of its predecessor.

Artificial Intelligence (AI) may seem like a vision for a distant future, but in truth, AI is all around us as machines are increasingly learning to sense, learn, reason, act and adapt in the real world. This is transforming industries and changing our lives in amazing new ways, by amplifying human capabilities, automating tedious or dangerous tasks, and solving some of our most challenging societal problems. In this article, we'll discuss the path to AI with Intel technologies. Let's take a closer look at AI's primary enabler machine learning as well as its younger sibling deep learning. While less than 10 percent of servers worldwide were deployed in support of machine learning last year [1], machine learning is the fastest growing field of AI and a key computational method for expanding the field of AI.

Intel MKL is a library of optimized math routines that are hand-optimized specifically for Intel processors. For example, it includes highly-optimized routines for Linear Algebra, Fast Fourier Transforms (FFT), Vector Math and Statistics functions. These mathematical operations are building blocks for machine learning and related analytic algorithms, and thus integration with MKL delivers massive performance boost for machine learning workloads. Spark is already instrumented to take advantage of optimized implementations of these routines using netlib-java, but still requires the addition of an implementation like MKL to activate these optimizations.

Get the Intel Math Kernel Library: http://intel.ly/2hu1qQb

Machine learning (ML) is a subset of the more general field of artificial intelligence (AI). ML is based on a set of algorithms that learn from data. Deep learning (DL) is a specialized ML technique that is based on a set of algorithms that attempt to model high-level abstractions in data by using a graph with multiple processing layers (https://en.wikipedia.org/wiki/Deep_learning). ML, and in particular DL, are currently used in a growing number of applications and industries, including image and video recognition/classification, face detection, natural language processing, and financial forecasting and prediction. A convenient way to work with DL is to use the Intel's optimized ML and DL frameworks. Using Intel optimized tools and frameworks to train and deploy deep networks guarantees that these tools will use Intel architecture in the most efficient way.

Click here to register and download your free 30-day trial of Intel Parallel Studio XE. We live in a world where humans rely more and more on computers to solve a variety of engineering problems―ranging from weather prediction to the discovery of lifesaving drugs. We are on the verge of another dramatic change where machines are capable of reaching and even exceeding humans in their ability to make decisions and solve complex problems. Computers have already beaten the best human players in Jeopardy* and Go*, and autonomous cars drive on the roads of California. This is all possible due to petaflop levels of compute power (thanks to Moore's Law) and the vast amounts of data available for training machine learning algorithms. At Intel, we work in close collaboration with our leading academic and industry fellow travelers to solve the hardware and software architectural challenges for Intel's upcoming multicore/manycore compute platforms. To help innovators tackle the complexities of machine learning, we are making performance optimizations available to developers through familiar Intel software tools, specifically through the Intel Data Analytics Acceleration Library (Intel DAAL) and enhancements to the Intel Math Kernel Library (Intel MKL).