IAS Seminar via Live Stream: "High-performance Communication Strategies in Parallel and Distributed Deep Learning"

Speaker: Prof. Dr. Torsten Höfler, Department of Computer Science, ETH Zürich

Abstract:

Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. Specifically, we present trends in DNN architectures and the resulting implications on parallelization strategies. We discuss the different types of concurrency in DNNs; synchronous and asynchronous stochastic gradient descent; distributed system architectures; communication schemes; and performance modeling. Based on these approaches, we extrapolate potential directions for parallelism in deep learning.

This talk was streamed via DFNConf and is now available at Youtube at

https://www.youtube.com/watch?v=uNzQ1vvJ82c