Heterogeneous systems - hardware software co-design


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


[Show all publications]

Using NETCONF for Automatic Fault Diagnosis in Time-Sensitive Networking (Sep 2024)
Ines Alvarez, Saad Mubeen, Mohammad Ashjaei
IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'24)

Redundancy Link Security Analysis:\\An Automation Industry Perspective (Sep 2024)
Björn Leander, Bjarne Johansson, Saad Mubeen, Mohammad Ashjaei, Tomas Lindström
IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'24)

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)

Autonomous Realization of Safety-and Time-Critical Embedded Artificial Intelligence (Mar 2024)
Joakim Lindén, Andreas Ermedahl, Hans Salomonsson , Masoud Daneshtalab, Bjorn Forsberg , Paris Carbon
Design, Automation & Test in Europe Conference (DATE'24)

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)

Masoud Daneshtalab, Professor

Phone: +4621103111

Saad Mubeen, Professor

Room: U1-142
Phone: +4621103191