With the chaotic and rapidly evolving landscape around deep learning, we need deep learning-specific compilers to enable maximum performance in a wide variety of use cases on a wide variety of hardware platforms. Jason Knight offers an overview of the Intel Nervana Graph project, which was designed to solve this problem.
Intel Nervana establishes a hardware-independent intermediate representation (IR) for deep learning that all deep learning frameworks can target, which allows them to seamlessly and efficiently execute across present and future platforms with minimal effort. In addition to this IR, the project offers connectors to popular frameworks such as TensorFlow, Intel’s reference framework neon, and backends for compiling and executing this IR on CPUs, GPUs, and emerging deep learning accelerators.
Jason Knight is senior technology officer at Intel, where he advances what is possible with machine learning using Intel Nervana. Jason holds a PhD in computational biology. His research included developing hierarchical Bayesian statistical models for classification of cancer tumor expression data and 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.
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