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|
|Farnam||Khalili Maybodi||Research Engineer/Technician|
|Joakim||Lindén||Industrial Doctoral Student|
|PROVIDENT: Predictable Software Development in Connected Vehicles Utilising Blended TSN-5G Networks||active|
|AutoDeep: Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles||active|
|Dependable AI in Safe Autonomous Systems||active|
|DESTINE: Developing Predictable Vehicle Software Utilizing Time Sensitive Networking||active|
|FASTER-ΑΙ: Fully Autonomous Safety- and Time-critical Embedded Realization of Artificial Intelligence||active|
|GreenDL: Green Deep Learning for Edge Devices||active|
|HERO: Heterogeneous systems - software-hardware integration||active|
|SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems||active|
|DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices||finished|
|Energy-Efficient Hardware Accelerator for Embedded Deep Learning||finished|
Timing Predictability and Performance Standoff in Component-based Vehicle Software on Multi-core (Mar 2023) Saad Mubeen IEEE International Conference on Software Architecture Companion Proceedings (MDE4SA@ICSA 2023)
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection (Sep 2022) Ali Zoljodi, Mohammad Loni, Sadegh Abadijou , Mina Alibeigi , Masoud Daneshtalab ICANN2022: 31st International Conference on Artificial Neural Networks (ICANN2022)
Implementing a First CNC for Scheduling and Configuring TSN Networks (Sep 2022) Ines Alvarez, Andreu Servera , Julián Proenza , Mohammad Ashjaei, Saad Mubeen International Conference on Emerging Technologies and Factory (ETFA'2022)
Migrating Legacy Ethernet-Based Traffic with Spatial Redundancy to TSN networks (Sep 2022) Mateu Jover , Manuel Barranco , Ines Alvarez, Julián Proenza International Conference on Emerging Technologies and Factory (ETFA'2022)
Schedulability Analysis of WSAN Applications: Outperformance of a Model Checking Approach (Sep 2022) Ehsan Khamespanah , Morteza Mohaqeqi , Mohammad Ashjaei, Marjan Sirjani International Conference on Emerging Technologies and Factory (ETFA'2022)