Heterogeneous systems - hardware software co-design
Focus:
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.
webpage: https://www.es.mdh.se/hero/
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
A Study of On-Device Deep Reinforcement Learning for Task Offloading under Dynamic 5G Channel Conditions (Sep 2025) Gorka Nieto , Idoia de la Iglesia , Unai LOPEZ , Cristina Perfecto , Mohammad Ashjaei, Ali Balador International Conference on Evaluation and Assessment in Software Engineering (ETFA'25)
Scheduling 5G Radio Resources for the Transmission of Real-time TSN Flows (Sep 2025) Zenepe Satka, Federico Aromolo , Mohammad Ashjaei, Alessandro Biondi , Daniel Casini , Hossein Fotouhi, Niccolo Borgioli , Masoud Daneshtalab, Mikael Sjödin, Saad Mubeen
proard: progressive adversarial robustness distillation: provide wide range of robust students (Jul 2025) Seyedhamidreza Mousavi, Seyedali Mousavi, Masoud Daneshtalab International Joint Conference on Neural Networks 2025 (IJCNN 2025)
Machine Learning-Based Prognostic Approaches for Construction Equipment Powertrain Systems (Jun 2025) Zafer Yigit, Håkan Forsberg, Masoud Daneshtalab 36th IEEE Intelligent Vehicles Symposium (IEEE IV2025)
Problem-Based Learning in an Educational and Training Module on Model-Based Development of Vehicle Software (Jun 2025) Saad Mubeen, Mohammad Ashjaei International Conference on Evaluation and Assessment in Software Engineering (EASE'25)
An Efficient Architecture for Edge AI Federated Learning with Homomorphic Encryption (Jun 2025) Dadmehr Rahbari , Masoud Daneshtalab, Maksim Jenihhin Journal of IEEE Access (IEEE-Access)
