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.
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.
Ongoing research projects
In this joint project, we aim at decreasing the power consumption and computation load of the current image processing platform by employing the concept of computation reuse.
Project manager at MDU: Masoud Daneshtalab
Main financing: STINT - The Swedish Foundation for International Cooperation in Research and Higher Education
The overall goal of the project is: to develop new techniques, methods, frameworks and tools to provide a full-fledged holistic software development, deployment and execution environment for vehicular systems that utilise a blend of TSN-5G networks for predictable communication, i.e., TSN as the backbone network within vehicles and 5G among the vehicles and their control centre.
Project manager at MDU: Saad Mubeen
Main financing: Vinnova
This project addresses design methods for the use of DNNs in airborne safety-critical systems.
Project manager at MDU: Håkan Forsberg
Main financing: Vinnova