State-Of-The Art Machine Learning Algorithms and How They Are Affected By Near-Term Technology Trends

State-Of-The Art Machine Learning Algorithms and How They Are Affected By Near-Term Technology Trends

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Rob Farber, TechEnablement
Fine Hall 224

Industry and Wall Street projections indicate that Machine Learning will touch every piece of data in the data center by 2020. This has created a technology arms race and algorithmic competition as IBM, NVIDIA, Intel, and ARM strive to dominate the retooling of the computer industry to support ubiquitous machine learning workloads over the next 3-4 years. Similarly, algorithm designers compete to create faster and more accurate training and inference techniques that can address complex problems spanning speech, image recognition, image tagging, self-driving cars, data analytics and more. The challenges for researchers and technology providers encompass big data, massive parallelism, distributed processing, and real-time processing.  Deep-learning and low-precision inference (based on INT8 and FP16 arithmetic) are current hot topics.This talk will merge two state-of-the-art briefings.
Massive scale and state-of-the art algorithm mappings for both machine learning and unstructured data analytics including how they are affected by current and forthcoming hardware.
The technology trends at Intel (the Intel® Scalable Systems Framework including both Intel Xeon Phi Knights Landing and Knights Mill plus the Skylake Purely uArch), NVIDIA (Pascal GPUs including P100 “training” and the 8-bit arithmetic “inference” optimized GPUs), IBM (Power8/9 plus TrueNorth “bee-brain on a chip”), ARM and OpenPower that will affect algorithm developments.
The goal is to give attendees a sense of the fast-track algorithm + technology combinations for both research and commercial success as well as an overview of the state-of-the-industry and near-term industry directions.