Dr. Xipeng Shen
Professor, Department of Computer Science
North Carolina State University
Topic: Deep Reuse for Deep Learning: Reuse-Centric Optimizations of Machine Learning
This talk presents deep reuse, a reuse-centric approach to accelerating Deep Learning in both training and inference. Reuse-centric optimization centers around harnessing reuse opportunities for enhancing computing efficiency. It generalizes the principle of computation reuse to a high level and a large scope through a synergy between programming systems and machine learning algorithms. Its exploitation of computation reuse spans across the boundaries of machine learning algorithms, implementations, and infrastructures; the types of reuse it covers range from pre-trained Neural Network building blocks to preprocessed results and even memory bits; the benefits it generates extend from orders of magnitude faster search for a good smaller Convolution Neural Network (CNN) to the elimination of all space cost in protecting parameters of CNNs.