Abstract: I started using OSGi starting my PhD in Ghent University in 2009. Since then a lot of things changed: 2 new OSGi releases, bndtools was developed, the microservices hype started, ... However, 8 years later I am still using OSGi in my day-to-day projects. In this talk I will cover my experience with OSGi starting back in 2009, and show how I used it in various research projects, ranging from augmented reality, robots and machine learning. Speaker Bio: Tim Verbelen received his M.Sc. degree in Computer Science from Ghent University, Belgium in June 2009. In July 2013, he received his Ph.D. degree with his dissertation "Adaptive Offloading and Configuration of Resource Intensive Mobile Applications". In October 2014, he joined iMinds IoT lab working on distributed intelligence for supporting the next generation IoT applications. As of October 2016, he works as senior researcher at imec on deep learning in distributed and resource-constrained environments. Since 2013, Tim is a committer of the Eclipse Foundation, contributing to the Concierge project, a lightweight OSGi core implementation. As of October 2014, he is also active as Invited Researcher of the OSGi Alliance.

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OSGI: FROM AMATEUR TO INVITED CONTRIBUTOR
TIM VERBELEN

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DISTRIBUTED COMPUTING: MODULARITY IS KEY

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DISTRIBUTED COMPUTING: MODULARITY IS KEY

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AUGMENTED REALITY IS COMING
ARKIT/ARCORE
Apple ARKit
Google ARCore

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MOBILE OFFLOADING
VideoSource Renderer
Tracker Mapper
Object
Recognizer
SPLITTING UP MOBILE AUGMENTED REALITY APPLICATIONS
T. Verbelen, T. Stevens, P. Simoens, F. De Turck, B. Dhoedt - Dynamic deployment and quality
adaptation for mobile augmented reality applications - Journal of Systems and Software, 2011

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ECLIPSE CONCIERGE
A TINY OSGI RUNTIME
Goals:
 Full OSGi core R5 compatibility
 Keep a small footprint to work well on
embedded devices
 Remain “readable”
 Being a sandbox for innovation around OSGi

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EDGE-CLOUD COMPUTING
FINE GRAINED SCALING ON COMPONENT LEVEL
S. Bohez, T. Verbelen, P. Simoens, B. Dhoedt - Enabling Component-based Mobile Cloud
Computing with the AIOLOS Middleware – ARM 2014

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CLUSTERINFO SPEC
MANAGEMENT AND MONITORING OF DISTRIBUTED OSGI NODES

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THE RISE OF AI
THE UNREASONABLE EFFECTIVENESS OF NEURAL NETWORKS
AlexNet (2012)
Overfeat (2014)
VGG-19 (2014)
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20
40
60
80
100
Top-5Accuracy(%)
AlexNet (2012)
Overfeat (2014)
VGG-19 (2014)
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5
10
15
20
25
billionsofops

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DIANNE
E. De Coninck, T. Verbelen, B Vankeirsbilck, S. Bohez, S. Leroux, P. Simoens - DIANNE: Distributed
Artificial Neural Networks for the Internet of Things – M4IoT 2015
DISTRIBUTED ARTIFICIAL NEURAL NETWORKS

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DIANNE
DISTRIBUTED ARTIFICIAL NEURAL NETWORKS
 Modular, distributed deep learning framework in OSGi
 Builds on top of Torch, exploiting highly optimized CPU and
GPU operations
 Transparent distributed neural network inference and
training
 Web UI to quickly prototype and experiment

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TEACHING YOUR ROBOT NEW TRICKS
AI+ROBOTS = FUN
LIDAR laser
range finder
Nvidia Jetson TX
embedded GPU

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LEARNING ACTIONS FROM ROBOT INPUT
Neural NetworkNeural Network
Reward signal: negative distance to the can
train

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SCALING DEEP REINFORCEMENT LEARNING
SimulationSimulation
SimulationSimulation
SimulationSimulation
SimulationSimulation
Experience
Pool
LearnerLearner
Neural
Network
Repository

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THANK YOU
http://dianne.intec.ugent.be/
http://www.eclipse.org/concierge
http://aiolos.intec.ugent.be/
tim.verbelen@ugent.be

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