Deep Learning Acceleration Techniques for Real Time Mobile Vision Applications
This addresses the problem of enabling real-time mobile vision applications for end-users, but it appears incremental as it focuses on summarizing existing acceleration techniques rather than introducing a new method.
The paper tackles the challenge of implementing deep neural networks for real-time visual recognition on mobile devices, which are too heavy for current hardware, by presenting acceleration techniques to enable these applications.
Deep Learning (DL) has become a crucial technology for Artificial Intelligence (AI). It is a powerful technique to automatically extract high-level features from complex data which can be exploited for applications such as computer vision, natural language processing, cybersecurity, communications, and so on. For the particular case of computer vision, several algorithms like object detection in real time videos have been proposed and they work well on Desktop GPUs and distributed computing platforms. However these algorithms are still heavy for mobile and embedded visual applications. The rapid spreading of smart portable devices and the emerging 5G network are introducing new smart multimedia applications in mobile environments. As a consequence, the possibility of implementing deep neural networks to mobile environments has attracted a lot of researchers. This paper presents emerging deep learning acceleration techniques that can enable the delivery of real time visual recognition into the hands of end users, anytime and anywhere.