DPAC - Dependable Platforms for Autonomous systems and Control

Status:

active

Start date:

2015-09-01

End date:

2023-08-31

The DPAC profile establishes a leading research profile targeting dependable platforms for autonomous systems and control, hosted at Mälardalen University in the Embedded Systems (ES) research environment. This will be accomplished through close collaboration and interaction between ES research groups at MDH and the participating industrial companies. The profile will leverage our solid track record of close cooperation to conduct excellent research, knowledge transfer, and support commercialization with industrial partners. DPAC shall create synergy effects between the partners and a significant increase in coproduction is to be expected.

The ultimate goal of the DPAC profile is to establish a nationally leading and internationally renowned research centre that facilitate close cooperation between academia and industry to achieve a significant increase in research and available knowhow on advanced dependable platforms for embedded systems. Embedded computer systems are nowadays incorporated in many kinds of products including many mission critical applications such as trains, autonomous utility vehicles, aviation, smart grid power management etc. These systems offer advanced functionality and serve an important role for the competitiveness of companies and the future national and global infrastructure. The scientific and technical results of DPAC will support future innovation by providing dependable platforms that can be used to efficiently realize dependable, reliable and safe electronically controlled products.

Four established research groups from MDH will in addition to the staff from companies provide the core competence thrust within DPAC. The research will be organized around three main research areas:

  • Predictability and dependability in parallel architectures
  • Autonomous systems and control
  • Design methodologies

These combined competences give DPAC a unique opportunity to address system-wide research challenges that span several traditional research areas and wide industrial applications as well as forming a robust basis for the research in DPAC.

DPAC brings a wide industrial participation ranging from small-medium enterprises to large multinational corporations. The initial industrial partners are; ABB CRC, ABB Control Technologies, Alten, Arcticus Systems, Bombardier Transportation, BAP, Enea, Ericsson, Hök Instruments, Saab, Volvo Construction Equipment, and Volvo Group Trucks Technology. These companies represent the core of this proposal’s research target and will bring their unique competence and relevant use-cases to facilitate and strengthen the research within DPAC.

DPAC allows a unique opportunity for ES to focus established researchers and new recruits towards the area of dependable systems and platforms. This area is identified as key-area for future growth in both education and research, and where industrial support is already large and anticipated to grow further during the coming decade.

[Show all publications]

Adaptive Autonomy in a Search and Rescue Scenario (Jan 2019)
Mirgita Frasheri , Baran Çürüklü, Mikael Ekström, Alessandro Papadopoulos
12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2018)

Timing Analysis Driven Design-Space Exploration of Cause-Effect Chains in Automotive Systems (Oct 2018)
Matthias Becker, Saad Mubeen
44th Annual Conference of the IEEE Industrial Electronics Society (IECON'18)

Designing Compact Convolutional Neural Network for Embedded Stereo Vision Systems (Sep 2018)
Mohammad Loni, Amin Majd , Abdolah Loni , Masoud Daneshtalab, Mikael Sjödin, Elena Troubitsyna
IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC-2018)

Enforcing Quality of Service Through Hardware Resource Aware Process Scheduling (Sep 2018)
Marcus Jägemar, Sigrid Eldh, Björn Lisper, Moris Behnam, Andreas Ermedahl
International Conference on Emerging Technologies and Factory Automation (ETFA'18)

Mallocpool: Improving Memory Performance Through Contiguously TLB Mapped Memory (Sep 2018)
Marcus Jägemar
International Conference on Emerging Technologies and Factory Automation (ETFA'18)

ADONN: Adaptive Design of Optimized Deep Neural Networks for Embedded Systems (Aug 2018)
Mohammad Loni, Masoud Daneshtalab, Mikael Sjödin
21st Euromicro Conference on Digital System Design (DSD'18)