Course Syllabus for

Real-time and Embedded Systems with Applications to Machine Learning
Realtids- och inbyggda system med tillämpningar mot maskininlärning

FRT160F, 5 credits

Valid from: Spring 2017
Decided by: Professor Thomas Johansson
Date of establishment: 2017-02-09

General Information

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

Course Contents

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.

Course Literature

Buttazzo, G.: Hard Real-Time Computing Systems. Springer, 2011.
A reading list of research papers will be provided in class.

Instruction Details

Type of instruction: Lectures

Examination Details

Examination format: Written exam
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: Basic knowledge on real-time systems from, e.g., FRTN01 Real-Time Systems.

Further Information

The course is given in the form of six half-day lectures and has a final written exam.

Course Occasion Information

Contact and Other Information

Course coordinators:

Web page:

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