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
|Industrial Doctoral Student
|PROVIDENT: Predictable Software Development in Connected Vehicles Utilising Blended TSN-5G Networks
|AutoDeep: Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles
|AutoFL: Cross-Layer Trusted Systems for Heterogeneous Federated Learning at Scale
|Dependable AI in Safe Autonomous Systems
|Developing Predictable and Secure IoT for Autonomous Systems
|FASTER-ΑΙ: Fully Autonomous Safety- and Time-critical Embedded Realization of Artificial Intelligence
|GreenDL: Green Deep Learning for Edge Devices
|RELIANT Industrial graduate school: Reliable, Safe and Secure Intelligent Autonomous Systems
|SEINE:Automatic Self-configuration of Industrial Networks
|DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices
|DESTINE: Developing Predictable Vehicle Software Utilizing Time Sensitive Networking
|Energy-Efficient Hardware Accelerator for Embedded Deep Learning
|HERO: Heterogeneous systems - software-hardware integration
|SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems
Task Offloading in Edge-cloud Computing using a Q-Learning Algorithm (Jun 2024) Somayeh Abdi, Mohammad Ashjaei, Saad Mubeen The International Conference on Cloud Computing and Services Science (CLOSER 2024)
A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks (Jan 2024) Mohammad Ahmadilivani , Mahdi Taheri , Jaan Raik , Masoud Daneshtalab, Maksim Jenihhin ACM Computing Surveys (CSUR)
Server Time Reservation for Periodic Real-Time Applications (Dec 2023) Ali Balador, Lizzy Tengana , Mohammad Ashjaei, Saad Mubeen The First International Workshop on Intelligent Systems and Paradigms for Next Generation Computing Evolution (INSPIRE'23)
Dynamic Priority Scheduling for periodic systems using ROS 2 (Oct 2023) Lukas Dust, Saad Mubeen 8th International Conference on Engineering of Computer-based Systems (ECBS2023)
Experimental Analysis of Wireless TSN Networks for Real-time Applications (Oct 2023) Zenepe Satka, Deepa Barhia , Sobia Saud , Saad Mubeen, Mohammad Ashjaei 28th International Conference on Emerging Technologies and Factory Automation (ETFA 2023)
Pattern-Based Verification of ROS 2 Nodes using UPPAAL (Sep 2023) Lukas Dust, Rong Gu, Cristina Seceleanu, Mikael Ekström, Saad Mubeen FMICS 2023 - International Conference on Formal Methods for Industrial Critical Systems (FMICS 2023)