Intel® Optimization for Deep Learning Frameworks
This session helps you learn the Intel® Optimization for TensorFlow* and Intel® Optimization for PyTorch*. Demonstrations on both products give:
- Methodologies of Intel® Optimizations
- Guides for installation
- Education on contributions to a performance boost for Intel® Xeon® Scalable processors
- Exercises that show how to use these Intel Optimizations
Get a closer look at optimization methodologies from Intel (like operator optimization and fusion, parallelism, and vectorization) on the most widely used deep learning frameworks: TensorFlow and PyTorch.
A brief introduction to the Intel® oneAPI Deep Neural Network Library (oneDNN) shows how to accelerate the performance of common deep learning operators (such as convolution and pooling) on Intel® platforms.
Jing Xu is a technical consulting engineer for Intel® software tools and high-performance libraries on Intel® architecture. He joined Intel about five years ago. Jing has experience in enabling global developers, enterprise users, engineers, and researchers to use embedded Intel® tools that include Intel® Math Kernel Library, oneDNN, and deep learning frameworks. His research interests include machine learning, deep learning, and research and development for performance optimization and data analysis.
Aditya Sirvaiya is an AI software solutions engineer in Intel's Artificial Intelligence and Analytics (AIA) group. He helps AI developers and engineers get the best performance out of Intel® platforms by using Intel® AI software and oneAPI high-performance libraries. Aditya has a bachelor's degree in engineering physics from the Indian Institute of Technology (IIT) Delhi and a master's degree in computer science (specializing in AI) from IIT Bombay.
Vishnu Madhu is an AI software solutions engineer at Intel and is based out of Bangalore, India. He is an EEE graduate with more than a decade of technology experience. Vishnu's past work involved connecting machine learning systems for use cases that span computer vision, natural language processing (NLP), and recommender systems. Currently, he enables customers to efficiently use Intel® hardware for deploying AI and machine learning applications. You might cross paths with him at AI evangelization events, hackathons, and, other technical conferences.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.