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|
|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|
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)
Challenges in Using Neural Networks in Safety-Critical Applications (Oct 2020) Håkan Forsberg, Johan Hjorth, Masoud Daneshtalab, Joakim Lindén , Torbjörn Månefjord The 39th Digital Avionics Systems Conference (DASC'2020)
MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification (Oct 2020) Seyed Ahmad Mirsalari , Sima Sinaei, M Ersali, Masoud Daneshtalab IEEE transaction on circuits and Systems (ISCAS)
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)