Time Activity
8:00 - 9:00 Registration & Poster Setup
9:00 – 9:45 Invited Talk by Prof. Dr. Marco Platzner: "Self-awareness at the Level of Heterogeneous Compute Nodes"
9:45 - 10:45 Theme I - Application: Presentations from Participants (Elevator Pitch Style), Open Discussion, and Poster Viewing
10:45 – 11:15 Networking Coffee Break
11:15 - 12:15 Theme II - Autonomy: Presentations from Participants (Elevator Pitch Style), Open Discussion, and Poster Viewing
12:15 - 13:30 Lunch Break
13:30 - 14:30 Theme III - Interaction: Presentations from Participants (Elevator Pitch Style), Open Discussion, and Poster Viewing
14:30 – 15:00 Networking Break & Poster Viewing
15:00 - 16:00 Theme IV - Cognitive Foundations: Presentations from Participants (Elevator Pitch Style), Open Discussion, and Poster Viewing
16:00 – 16:30 Networking Coffee Break & Poster Viewing
16:30 – 17:30 Panel Discussion
17:30 – 18:00 Closing Session: Conclusion and Future Plans
19:00 Dinner at La Galleria

Heterogeneous compute nodes combine different types of processors with reconfigurable hardware cores to exploit trade-offs in performance, energy and cost. The operation of such compute nodes under varying workloads, objectives and system conditions poses a great challenge, making this near-hardware level of computing systems a promising target for self-awareness research. In this talk, I will first introduce to the architecture and programming model for our heterogenous compute nodes. Then, I will briefly overview the EPiCS project and show how the reference architecture developed there helped us create advanced prototypes. Finally, I will mention planned future work applying and further developing self-awareness concepts and design patterns in the domains of heterogeneous high-performance computing and micro aerial vehicles.

To learn more about this talk please check the presentation slides here

Theme I: Application

Author(s) Title
Charles P. Martin, Kyrre Glette, Tonnes F. Nygaard, and Jim Torresen Self-Awareness in a Cyber-Physical Predictive Musical Interface
Marwin Züfle, André Bauer, Veronika Lesch Predictive Maintenance for Industry 4.0
Sai Manoj Pudukotai Self-Aware Resource Management for Many-Core Microprocessors
Veronika Lesch, André Bauer, Marwin Züfle Towards Self-Aware Industry 4.0 using a Layered Meta-Model
Maximilian Götzinger, Arman Anzanpour, Iman Azimi, Nima TaheriNejad, and Amir M. Rahmani Self-Awareness in Remote Health MonitoringSystems using Wearable Electronics

Theme II: Autonomy

Author(s) Title
Ada Diaconescu and Jeremy Pitt Self-awareness and Decision-taking in Socio-Cyber-Physical Systems: An Architectural Perspective
Henner Heck Self-Tests for Self-Awareness in Distributed Cyber-Physical Systems
Christoph Lüth, Martin Ring, Rolf Drechsler Self-Verification for Self-Aware Systems
Nazmul Hussain, Hai H Wang, C.D. Buckingham Policy-based Generic Autonomic Adapter and Knowledge Model for A Cyber-Physical Social System: GRaCE Case Study
Andreas Herkersdorf, Nikil Dutt, Rolf Ernst, Fadi Kurdahi Design Methodologies for enabling Self-awareness in Embedded Systems

Theme III: Interaction

Author(s) Title
Leandro Soares Indrusiak Selfish and altruistic behaviour in resource-constrained cyber-physical systems using pheromone signalling
Chloe M. Barnes, Thomas D. Griffiths, Anikó Ekárt, and Peter R. Lewis A Family of Environments for Exploring Social Self-Awareness
Adrian Calma, Christian Gruhl, Sven Tomforde, and Bernhard Sick Cyber-physical Systems with Humans in the Loop
Arezoo Vejdanparast, Peter R. Lewis, Lukas Esterle Be Aware of Your Limitations

Theme IV: Cognitive Foundations

Author(s) Title
Christian Gruhl, Sven Tomforde, and Bernhard Sick Self-Awareness through Novelty Detection
Ulysses Bernardet and Daniele Mazzei A Mind for IoT
Chris Landauer, and Kirstie L. Bellman Self-Modifying is Necessary for Cyber-Physical Systems, and Easier Than You Think
Thomas D. Griffiths and Chloe M. Barnes Self-Adaptive Task Separation in the Tartarus Problem
Madis Kerner, Kalle Tammemäe, and Thomas Hollstein Self-timing of sampling to reduce the complexity of the model

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