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|
|DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices||active|
|DESTINE: Developing Predictable Vehicle Software Utilizing Time Sensitive Networking||active|
|Energy-Efficient Hardware Accelerator for Embedded Deep Learning||active|
|HERO: Heterogeneous systems - software-hardware integration||active|
SoFA: A Spark-oriented Fog Architecture (Oct 2019) Neda Maleki , Mohammad Loni, Masoud Daneshtalab, Mauro Conti , Hossein Fotouhi IEEE 45th Annual Conference of the Industrial Electronics Society (IECON'19)
Optimising Vehicular System Architectures with Real-time Requirements: An Industrial Case Study (Oct 2019) Arman Hasanbegovic , Marcus Ventovaara , Jimmie Wiklander, Saad Mubeen IEEE 45th Annual Conference of the Industrial Electronics Society (IECON'19)
NeuroPower: Designing Energy Efﬁcient Convolutional Neural Network Architecture for Embedded Systems (Sep 2019) Mohammad Loni, Ali Zoljodi , Sima Seenan, Masoud Daneshtalab, Mikael Sjödin The 28th International Conference on Artificial Neural Networks (ICANN 2019)
Multi-objective Optimization of Real-Time Task Scheduling Problem for Distributed Environments (Sep 2019) Maghsood Salimi , Amin Majd , Mohammad Loni, Tiberiu Seceleanu, Cristina Seceleanu, Marjan Sirjani, Masoud Daneshtalab, Elena Troubitsyna 6th Conference on the Engineering of Computer Based Systems (ECBS 2019)
TOT-Net: An Endeavor Toward Optimizing Ternary Neural Networks (Sep 2019) Najmeh Nazari , Mohammad Loni, Mostafa E. Salehi , Masoud Daneshtalab, Mikael Sjödin 22nd Euromicro Conference on Digital System Design (DSD 2019)
Holistic Modeling of Time Sensitive Networking in Component-based Vehicular Embedded Systems (Aug 2019) Saad Mubeen, Mohammad Ashjaei, Mikael Sjödin Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2019)