• Design process for embedded systems. Design requirements.
• The platform for embedded systems and its components.
• Analysis and optimisation of software for embedded systems.
• Periodic process model, basic scheduling algorithms and scheduling analysis.
• Parallel processes and communication mechanisms.
• Realtime operating systems.
• Acceleration of the system through additional hardware. Co-design of hardware and software.

Having passed the course, the student should be able to

• describe the fundamental structure of the platform for embedded computer systems and explain cooperation between the software and the hardware components
• analyse how architecture and implementation decisions influence the performance in an embedded system
• use basic models and analytical methods for embedded realtime systems
• develop software for simple embedded real time systems

in order to obtain a good understanding of the design process for embedded systems and basic methods and technologies for the design of embedded systems.

The course will use written course material, which will be distributed in Canvas as 'Lecture Notes'.

Students at KTH with a permanent disability can get support during studies from Funka:

Funka - compensatory support for students with disabilities

• TEN1 - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
• LAB1 - Laboratory Work, 3.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

If the course is discontinued, students may request to be examined during the following two academic years.

No use of generative AI tools allowed

All use of generative AI is prohibited in the course's examination and graded assignments. You are also advised to avoid the use of generative AI in your own studies and during class time. This is based on the fact that the use of generative AI is considered to have a negative impact on students' ability to meet the course's learning objectives.

The ban on generative AI means that

• All examination and graded assignments must be carried out without the help of generative AI.
• All submissions must be completely human-generated.

You as a student are fully responsible for all material you submit and must be able to defend and explain it completely without the support of generative AI.

Students are also advised to avoid using generative AI for support and guidance during their own study, as the answers are unreliable and may omit important parts of the course. Instead, seek support from your fellow students and teachers for guidance and support, and use the course literature for facts.

Disciplinary action for unauthorized use of AI

Everything you submit must be your own work. Using generative AI in the course's examination and graded assignments is considered unauthorized assistance and may result in disciplinary action.

Questions or concerns?

Contact the course responsible for guidance and clarification of this information, or if you are wrongly accused of using generative AI in the course's examination and graded assignments.

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

The official course syllabus is valid from the autumn semester 2021 in accordance with Head of School decision: J-2021-0878.

Decision date: 15/04/2021