Model Optimization Techniques for Embedded Artificial Intelligence

Deep learning has made remarkable progress in computer vision and neural language processing tasks. CNNs has become the most popular structure due to its high accuracy and end-to-end training ability. However, the complex CNNs models bring high computing and storage cost which make it difficult to be deployed on various hardware platforms. Tremendous computations also become a barrier in achieving real-time performance. In order to solve these problems, recent studies have proposed significant techniques to compress the model to minimize the computation and storage intensity. Popular model optimization techniques include network pruning, low precision quantization and dynamic inference. In this paper, we compare and discuss state-of-the-art methods within the range of these three methods as a way to guide software and hardware developers to select the best method for their objective.