The group aims to boost exploitation of heterogeneous systems in terms of predictability, effective development and efficient software-hardware integration for next-generation intelligent embedded systems.
webpage: https://www.es.mdh.se/hero/
With the exploding need for high-performance computing, we are at the dawn of the heterogeneous era, where all future computing platforms are likely to embrace heterogeneity. In a heterogeneous system, there can be several different computational units such as multi-core central processing units (CPUs), graphics processing units (GPUs), field-programmable gate arrays (FPGAs), digital signal processing units (DSPs), and artificial intelligence (AI) accelerators/engines.
One major driving force for heterogeneous systems is the next generation intelligent, adaptive and autonomous systems that will form the base for coming products like autonomous vehicles and autonomous manufacturing.
With a diverse range of architectures (on a single chip or distributed), a main challenge is to make use of the enormous computational power in the best way, while still meeting several criteria like performance, energy efficiency, time predictability, and dependability.
The overall goal of this research group is to tackle the following scientific areas:
• Hardware/software co-design and integration
• System architecture and specialization
• AI and deep learning acceleration
• Model-based development of predictable software architectures
• Pre-runtime analysis of heterogeneous embedded systems
First Name | Last Name | Title |
---|---|---|
Aldin | Berisa | Doctoral student |
Ali | Zoljodi | Doctoral student |
Amin | Majd | |
Björn | Lisper | Professor |
Bostan | Khan | Doctoral student |
Farnam | Khalili Maybodi | Research Engineer/Technician |
Håkan | Forsberg | Senior Lecturer |
Ines | Alvarez | |
Joakim | Lindén | Industrial Doctoral Student |
Johan | Hjorth | Doctoral student |
Kasra | Ekrad | Doctoral student |
Madiha | Umar | Doctoral student |
Mahdi | Taheri | Doctoral student |
Mainak | Chakraborty | Post Doc |
Masoud | Daneshtalab | Professor |
Mehdi | Modarressi | |
Mikael | Sjödin | Professor,Research Leader |
Mohammad | Ahmadilivani | Doctoral student |
Mohammad | Ashjaei | Associate Professor,Docent |
Mohammad | Riazati | |
Mostafa | Salehi | |
Nandinbaatar | Tsog | |
Obed | Mogaka | Doctoral student |
Saad | Mubeen | Professor |
Sahar | Mobaiyen | Doctoral student |
Sebastian | Leclerc | Doctoral student |
Seyedhamidreza | Mousavi | Doctoral student |
Sima | Sinaei | Post Doc |
Somayeh | Abdi | |
Zafer | Yigit | Industrial Doctoral Student |
Zenepe | Satka | Doctoral student |
Bridging the Gap: An Interface Architecture for Integrating CAN and TSN Networks (Dec 2024) Aldin Berisa, Saad Mubeen, Masoud Daneshtalab, Mohammad Ashjaei, Mikael Sjödin, Benjamin Kraljusic , Nejla Zahirovic MRTC Report, Mälardalen Real-Time Research Centre (MRTC 2024)
The Importance of a System-Level Approach when Bringing in New Technologies in Avionics (Oct 2024) Håkan Forsberg, Kristina Forsberg, Joakim Lindén 43rd Digital Avionics Systems Conference (DASC) (DASC'43)
Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments (Oct 2024) Joakim Lindén, Giovanni Burresi , Håkan Forsberg, Masoud Daneshtalab, Ingemar Söderquist 43rd Digital Avionics Systems Conference (DASC) (DASC'43)
Towards Developing a Supervisory Agent for Adapting the QoS Network Configurations (Sep 2024) Sebastian Leclerc, Kasra Ekrad, Bjarne Johansson, Ines Alvarez, Mohammad Ashjaei, Saad Mubeen IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'24)
Using NETCONF for Automatic Fault Diagnosis in Time-Sensitive Networking (Sep 2024) Ines Alvarez, Saad Mubeen, Mohammad Ashjaei IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'24)
Redundancy Link Security Analysis:\An Automation Industry Perspective (Sep 2024) Björn Leander, Bjarne Johansson, Saad Mubeen, Mohammad Ashjaei, Tomas Lindström IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'24)