Keynote Talk 1: Efficient DNN Training at Scale: from Algorithms to Hardware

Abstract

The recent popularity of deep neural networks (DNNs) has generated a lot of research interest in performing DNN-related computation efficiently. However, the primary focus of systems research is usually quite narrow and limited to inference (i.e., how to efficiently execute already trained models) and image classification networks as the primary benchmark for evaluation. In this talk, we will demonstrate a holistic approach to DNN training acceleration and scalability starting from the algorithm, to software and hardware optimizations, to special development and optimization tools.