Towards Efficient Architecture and System for Next Generation AI Applications
- Mingcong Song, University of Florida
- Time: 2018-04-02 10:50
- Host: Prof. Baoquan Chen
- Venue: Room 101, Courtyard No.5, Jingyuan
Abstract
In recent years, the artificial intelligence (AI) techniques, represented by deep neural networks (DNN), have emerged as indispensable tools in many fields, such as image and video recognition. Traditionally, due to its huge compute power and scalability, the cloud data center is often the best option for training and evaluating AI applications. With the increasing computing power and energy efficiency of mobile devices, there is a growing interest in performing AI applications on mobile platforms. As a result, we believe the next-generation AI applications are pervasive across all platforms, ranging from central cloud data center to edge-side wearable and mobile devices.
However, we observe several gaps that challenge the next generation AI applications. First, the diversity of computing hardware resources and different end-user requirements present challenges to AI applications deployment on various computing platforms, which results in inferior user satisfaction. Second, the traditional statically trained DNN model in cloud data center could not efficiently handle the dynamic data in the real in-situ environments, which leads to low inference accuracy. Lastly, the training of DNN models still involves extensive human efforts to collect and label the large-scale dataset, which becomes impractical in big data era where raw data is largely un-labeled and uncategorized.
Biography
Mingcong Song is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Florida. His research interests include architectural support for emerging AI applications, AI-enabled IoT system design and heterogeneous computing for machine learning and big data applications. His work has been published in top-tier conferences including ISCA, HPCA, ASPLOS, PACT, ICS, etc. His research has won the best paper nomination at HPCA 2017. He was the winner of Outstanding International Student Award at University of Florida in 2015. He received a BS degree from Huazhong University of Science and Technology in 2010 and an MS degree from University of Chinese Academy Sciences in 2013.