Intel® Nervana™ Graph: A Universal Tensor JIT Compiler Webinar

Register Today!

Deep learning is rapidly advancing traditionally hard problems in artificial intelligence such as image recognition, Go board game playing, speech recognition, machine translation, and more. It also drives a large increase in the appetite for high performance computational hardware. With the chaotic software landscape around deep learning and the deep learning accelerator space, you need to be able to depend on software that can enable maximum performance in a wide variety of customer use cases on a wide variety of hardware platforms.

The Intel® Nervana™ Graph project is designed to solve this problem by establishing an Intermediate Representation (IR) for deep learning that all frameworks can target which allows them to seamlessly and efficiently execute across the platforms of today and tomorrow with minimal effort. In addition to this IR, the project offers connectors to popular frameworks such as TensorFlow*, Intel’s reference framework named Neon™, and back-ends for compiling and executing this IR on IA, GPUs and future Intel hardware.

What you will learn:

  • Learn about the ecosystem of deep learning, how the field is paralleling the programming language community, and how Nervana™ Graph is a compiler for deep learning workloads to solve the combinatorial ecosystem dilemma.
  • Why build a deep learning compiler when we already have things like LLVM, CUDA*, cuDNN, Intel® MKL-DNN, and TensorFlow*?
  • What goes into making a deep learning compiler and what can end users expect from it?

Required Fields(*)

By submitting this form, you are confirming you are an adult 18 years or older and you agree to Intel contacting you with marketing-related emails or by telephone. You may unsubscribe at any time. Intel's web sites and communications are subject to our Privacy Notice and Terms of Use.

About Our Speaker

Jason Knight

Staff Algorithms Engineer, Intel

Jason has a PhD in computational biology where he developed hierarchical Bayesian statistical models for classification of cancer tumor expression data. In addition, he developed high performance Markov chain Monte Carlo techniques to discover gene regulatory networks in this data using Bayesian networks. He then applied these techniques on the world’s largest database of human genomes at Human Longevity Inc. before jumping into the heart of machine learning at Nervana to advance what is possible with machine learning.