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|
|DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices||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|
|Energy-Efficient Hardware Accelerator for Embedded Deep Learning||finished|
A Novel Frame Preemption Model in TSN Networks (Jun 2021) Mohammad Ashjaei, Mikael Sjödin, Saad Mubeen Journal of Systems Architecture (JSA)
Synthesising Schedules to Improve QoS of Best-effort Traffic in TSN Networks (Apr 2021) Bahar Houtan, Mohammad Ashjaei, Masoud Daneshtalab, Mikael Sjödin, Saad Mubeen 29th International Conference on Real-Time Networks and Systems (RTNS'21) (RTNS 2021)
An Automated Configuration Framework for TSN Networks (Mar 2021) Bahar Houtan, Albert Bergström , Mohammad Ashjaei, Masoud Daneshtalab, Mikael Sjödin, Saad Mubeen 22nd IEEE International Conference on Industrial Technology (ICIT'21) (ICIT 2021)
Image Synthesisation and Data Augmentation for Safe Object Detection in Aircraft Auto-Landing System (Feb 2021) Najda Vidimlic , Alexandra Levin , Mohammad Loni, Masoud Daneshtalab 16th International Conference on Computer Vision Theory and Applications (VISAPP 2021)
Optimizing Inter-Core Data-Propagation Delays in Industrial Embedded Systems under Partitioned Scheduling (Jan 2021) Lamija Hasanagic, Tin Vidovic, Saad Mubeen, Mohammad Ashjaei, Matthias Becker 26th Asia and South Pacific Design Automation Conference (ASP-DAC'21)