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|
|PROVIDENT: Predictable Software Development in Connected Vehicles Utilising Blended TSN-5G Networks||active|
|AutoDeep: Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles||active|
|DESTINE: Developing Predictable Vehicle Software Utilizing Time Sensitive Networking||active|
|HERO: Heterogeneous systems - software-hardware integration||active|
|SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems||active|
|DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices||finished|
|Energy-Efficient Hardware Accelerator for Embedded Deep Learning||finished|
A Comprehensive Exploration of Languages for Parallel Computing (Dec 2021) Federico Ciccozzi, Lorenzo Addazi, Sara Abbaspour Asadollah, Björn Lisper, Abu Naser Masud, Saad Mubeen ACM Computing Surveys (CSUR'21)
Implications of Various Preemption Configurations in TSN Networks (Oct 2021) Mohammad Ashjaei, Lejla Murselović , Saad Mubeen IEEE Embedded Systems Letters (Embed Syst Lett)
Curating Datasets for Visual Runway Detection (Oct 2021) Joakim Lindén , Håkan Forsberg, Josef Haddad , Emil Tagebrand , Erasmus Cedernaes , Emil Gustafsson Ek , Masoud Daneshtalab The 40th Digital Avionics Systems Conference (DASC'2021)
Scheduling Elastic Applications in Compositional Real-Time Systems (Sep 2021) Shaik Salman, Alessandro Papadopoulos, Filip Markovic, Saad Mubeen, Thomas Nolte 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2021)
Schedulability Analysis of Best-Effort Traffic in TSN Networks (Sep 2021) Bahar Houtan, Mohammad Ashjaei, Masoud Daneshtalab, Mikael Sjödin, Sara Afshar, Saad Mubeen 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2021)
Offloading Accelerator-intensive Workloads in CPU-GPU Heterogeneous Processors (Sep 2021) Nandinbaatar Tsog, Saad Mubeen, Fredrik Bruhn, Moris Behnam, Mikael Sjödin 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2021)