AI on Intel Architecture
This session offers an overview of the hardware and software which empower AI on Intel.
First, we will introduce the hardware features used for AI on Intel. We will then get a first glance at the software stack harnessing them, namely the Intel® oneAPI AI Analytics Toolkit.
Next, we will cover the Intel-optimized libraries for Machine Learning: Modin, Intel® Extension for Scikit-learn and XGBoost. Also we will look at the optimizations of Deep Learning frameworks such as Intel® Extension for PyTorch (IPEX) and Tensorflow (ITEX) powered by highly-optimized Intel® oneDNN. You will learn how to use the Intel extensions to enable extra features such as mixed-precision and running Deep Learning computations on Intel dGPU.
Last, We will also present the Intel Neural Compressor (i.e., a comprehensive software toolkit that offers a plethora of optimization for neural networks, such as quantization, pruning, graph optimizations.)
Speaker(s)
Vladimir Kilyazov - AI Software Solutions Engineer/Intel
Vladimir is an AI software solutions engineer with more than 5 years of experience in the AI field, who first joined Intel in 2017. He holds a Bachelor’s degree in Computer Science and a Master’s degree in Business Informatics from Higher School of Economics. Vladimir has worked in various fields including retail, medical, and enterprise. He has relevant experience in most popular AI domains such as computer vision, automatic speech recognition, machine learning. In addition to his day job, Vladimir mentors startups in Intel Ignite and Liftoff programs.
