Deep Learning Acceleration on Mobile Platforms

Although Deep Neural Networks (DNN) are ubiquitously utilized in many applications, it is generally difficult to deploy DNNs on resource-constrained devices, e.g., mobile platforms. In practical use, both testing (inference) phase and sophisticated training (learning) phase are required, calling for efficient testing and training methods with higher accuracy and shorter converging time. In this tutorial, we first introduce DNNs from a historical perspective and then present some representative techniques to reduce the computation cost of DNN, including network pruning, model compression, low precision design etc. In rial, we will show some examples to perform and optimize the training and testing of DNN on distributed mobile systems.
 

• Introduction to neural networks 

• History, structure, algorithms, and software