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:
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.
|First Name||Last Name||Title|
|Johan||Sundell||Industrial Doctoral Student|
|Youssef||Zaki||Industrial Doctoral Student|
Modelling multi-criticality vehicular software systems: evolution of an industrial component model (Jun 2020) Alessio Bucaioni, Saad Mubeen, Federico Ciccozzi, Antonio Cicchetti, Mikael Sjödin International Journal on Software and Systems Modeling (SoSyM'20)
A Systematic Migration Methodology for Complex Real-time Software Systems (May 2020) Shaik Salman, Alessandro Papadopoulos, Saad Mubeen, Thomas Nolte The 23rd International Symposium on Real-Time Distributed Computing (ISORC'20)
NOM: Network-On-Memory for Inter-Bank Data Transfer in Highly-Banked Memories (May 2020) Seyyed Hossein Seyyedaghaei Rezaei , Mehdi Modarressi, Rachata Ausavarungnirun , Mohammad Sadrosadati , Onur Mutlu , Masoud Daneshtalab IEEE Computer Architecture Letters (CAL)
Computation reuse-aware accelerator for neural networks (May 2020) Hoda Mahdiani , Alireza Khadem , Ali Yasoubi , Azam Ghanbari , Mehdi Modarressi, Masoud Daneshtalab Institution of Engineering and Technology (IET)
Hardware Acceleration for Recurrent Neural Networks (May 2020) Sima Sinaei, Masoud Daneshtalab Institution of Engineering and Technology (IET)
Feedforward Neural Networks on Massively Parallel Architectures (May 2020) Reza Hojabr , Ahmad Khonsari , Mehdi Modarressi, Masoud Daneshtalab Institution of Engineering and Technology (IET)
|ABB AB, Control Technologies||Industrial|
|ABB Corporate Research||Industrial|
|Alten Sverige AB||Industrial|
|Arcticus Systems AB||Industrial|
|Saab AB, Avionics Systems||Industrial|
|Volvo Construction Equipment AB||Industrial|
|Volvo Group Trucks Technology||Industrial|