Bandwidth-efficient deep learning
Song Han, William J. Dally · 2018
Deep learning algorithms are achieving increasingly higher prediction accuracy on many machine learning tasks. However, applying brute-force programming to data demands a huge amount of machine power to perform training and inference, and a huge amount of manpower to design the neural network models, which is inefficient. In this paper, we provide techniques to solve these bottlenecks: saving memory bandwidth for inference by model compression, saving networking bandwidth for training by gradient compression, and saving engineer bandwidth for model design by using AI to automate the design of models.