Valid from: Spring 2017
Decided by: Professor Thomas Johansson
Date of establishment: 2017-02-09
Division: Automatic Control
Course type: Third-cycle course
Teaching language: English
The course gives an overview of modern methods for scheduling and hardware/software co-design for real-time and embedded systems. There is a special focus on implementation of machine learning algorithms in resource-constrained systems.
Knowledge and Understanding
For a passing grade the doctoral student must
Competences and Skills
For a passing grade the doctoral student must
Introduction to real-time and embedded systems; Servers for handling aperiodic workloads; Limited preemptive and elastic scheduling; Multicore scheduling; Introduction to machine learning and artificial intelligence; Introduction to HW/SW codesign and partitioning; Neuromorphic computing with spiking neural networks.
Buttazzo, G.: Hard Real-Time Computing Systems. Springer, 2011.
A reading list of research papers will be provided in class.
Type of instruction: Lectures
Examination format: Written exam
Grading scale: Failed, pass
Examiner:
Assumed prior knowledge: Basic knowledge on real-time systems from, e.g., FRTN01 Real-Time Systems.
The course is given in the form of six half-day lectures and has a final written exam.
Course coordinators: