In the past several years, Deep Neural Networks (DNNs) have demonstrated record-breaking accuracy on a variety of artificial intelligence tasks. However, the intensive storage and computational costs of DNN models make it difficult to deploy them on the mobile and embedded systems for real-time applications. In this technical talk, Dr. Yao will introduce their recent works on deep neural network compression and acceleration, showing how they achieve impressive compression performance without noticeable loss of model prediction accuracy, from the perspective of pruning and quantization.