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.
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|
|DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices||active|
|DESTINE: Developing Predictable Vehicle Software Utilizing Time Sensitive Networking||active|
|Energy-Efficient Hardware Accelerator for Embedded Deep Learning||active|
|HERO: Heterogeneous systems - software-hardware integration||active|
|SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems||active|
|PROVIDENT: Predictable Software Development in Connected Vehicles Utilising Blended TSN-5G Networks||on-hold|
A software implemented comprehensive soft error detection method for embedded systems (Sep 2020) Seyyed Amir Asghari , Mohammadreza Binesh Marvasti , Masoud Daneshtalab Elsevier journal of Microprocessors and Microsystems (MICPRO)
Analysis of the TSN Standards for Utilization in Long-life Industrial Distributed Control Systems (Sep 2020) Daniel Hallmans, Mohammad Ashjaei, Thomas Nolte The 25th International Conference on Emerging Technologies and Factory Automation (ETFA2020)
A Systematic Literature Study on Definition and Modeling of Service-Level Agreements for Cloud Services in IoT (Aug 2020) Svetlana Girs, Séverine Sentilles, Sara Abbaspour Asadollah, Mohammad Ashjaei, Saad Mubeen IEEE Access (ACCESS'20)
DenseDisp: Resource-Aware Disparity Map Estimation by Compressing Siamese Neural Architecture (Jul 2020) Mohammad Loni, Ali Zoljodi , Daniel Maier , Amin Majd , Masoud Daneshtalab, Mikael Sjödin, Ben Juurlink , Reza Akbari IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2020 (IEEE WCCI)
Schedulability Analysis of Time-Sensitive Networks with Scheduled Traffic and Preemption Support (Jun 2020) Lucia Lo Bello , Mohammad Ashjaei, Gaetano Patti , Moris Behnam Journal of Parallel and Distributed Computing (JPDC2020)
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)