To build successful AI applications, developers must use highly optimized deep learning (DL) models—models that are developed and trained using DL frameworks such as TensorFlow* and MXNet*.
But until recently there’s been a challenge: most of these frameworks have been by default optimized only for GPUs, making CPUs a less attractive option for AI training.
To remedy that, Intel has developed several optimized DL computational functions (aka primitives) and integrated them into many popular frameworks to enable high performance for AI training on Intel-based devices. (The basic building blocks of the Intel® MKL-DNN library were at the heart of these optimizations.)
Join Louie Tsai, Intel Senior Software Engineer and embedded software specialist, to learn how these Intel-optimized frameworks can accelerate your AI applications on Intel® architecture.
Topics covered include:
- Introduction to Intel-optimized versions of popular frameworks like TensorFlow and MXNet
- A brief overview of the types of accelerations implemented on these frameworks
- How to acquire and use these framework packages with Intel’s accelerations
Get the software
For source code access and installation details visit: