Photo of Samuel S. Ogden

 Samuel S. Ogden

PhD Student
Computer Science Department
Worcester Polytechnic Institute
100 Institute Road
Worcester, MA 01609
Office: Fuller Labs B17

ssogden@wpi.edu

github.com/samogden


Publications [Google Scholar Profile]

MODI: Mobile Deep Inference Made Efficient by Edge Computing
Samuel S. Ogden, Tian Guo
The USENIX Workshop on Hot Topics in Edge Computing (HotEdge '18)
abstract

In this paper, we propose a novel mobile deep inference platform, MODI, that delivers good inference performance. MODI improves deep learning powered mobile applications performance with optimizations in three complementary aspects. First, MODI provides a number of models and dynamically selects the best one during runtime. Second, MODI extends the set of models each mobile application can use by storing high quality models at the edge servers. Third, MODI manages a centralized model repository and periodically updates models at edge locations ensuring up-to-date models for mobile applications without incurring high network latency. Our evaluation demonstrate the feasibility of trading off inference accuracy for improved inference speed, as well as the acceptable performance of edge-based inference.

Paper