The exponential growth in use of large, deep neural networks (DNN) has accelerated the need for training these networks in hours—even minutes.

This kind of speed cannot be achieved on a single machine—a single node cannot satisfy the compute, memory, and I/O requirements of today’s state-of-the-art DNNs.

The way to do it is through scalable and efficient distributed training, which is facilitated by deep-learning frameworks.

Join Intel® software engineer and deep-learning expert, Mikhail Smorkalov, for an overview of three Intel®-optimized deep-learning frameworks—Caffe*, Horovod* (for TensorFlow*), and nGraph—that boost communication performance on distributed workloads compared to existing approaches.

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Find out more about these optimized frameworks, including how to get them.

Mikhail Smorkalov
Software engineer, Intel Corporation

Mikhail specializes in deep-learning technologies. His responsibilities include defining deep-learning architecture, developing and deploying new features for the Intel® Machine Learning Scaling Library (Intel® MLSL) and scaling deep-learning workloads to some of the fastest supercomputers in the world.

Before joining Intel in 2014, Mikhail spent years developing software and middleware for the telecom industry. He holds a master of science in computational mathematics and cybernetics from the State University of Nizhni Novgorod.


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