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Module guide

Modul CS4720-KP06

Energy Efficiency in Emebedded Systems (EEE)

Duration:


1 Semester
Turnus of offer:


every summer semester
Credit points:


6
Course of studies, specific field and terms:
  • Master Robotics and Autonomous Systems 2019 (module part), Additionally recognized elective module, Arbitrary semester
  • Master Computer Science 2019 (optional subject), Elective, Arbitrary semester
Classes and lectures:
  • Energy Efficiency in Emebedded Systems (exercise, 2 SWS)
  • Energy Efficiency in Emebedded Systems (lecture, 2 SWS)
Workload:
  • 70 Hours in-classroom work
  • 85 Hours private studies and exercises
  • 25 Hours exam preparation
Contents of teaching:
  • Motivation and power dissipation on semiconductor level
  • Power dissipation of digital circuits, inparticular CMOS
  • Power Management in Hard- and Software (Sleep Modes, DVS, FS, Undervolting)
  • Energy efficient system design (applications)
  • Energy Harvesting and Transiently Powered Computing (TPC)
Qualification-goals/Competencies:
  • students will have a deeper understanding of hardware and software mechanisms for evaluating and developing energy-efficient embedded systems
  • They have a deeper understanding of the electrotechnical basics of power dissipation in digital systems
  • They can analyze the power dissipation of systems at any level and apply appropriate methods to increase efficiency
  • They can use a variety of standard techniques to achieve
  • They can model and evaluate energy-autonomous systems
Grading through:
  • Written or oral exam as announced by the examiner
Responsible for this module:
Teachers:
  • Dr. Ulf Kulau
Literature:
  • Ulf Kulau: Course: Energy Efficiency in Embedded Systems–A System-Level Perspective for Computer Scientists - EWME, 2018
  • David Harris and N. Weste: CMOS VLSI Design ed. - Pearson Education, 2010
  • Jan Rabaey: Low Power Design Essentials (Integrated Circuits and Systems) - Springer, 2009
Language:
  • English, except in case of only German-speaking participants
Notes:

Prerequisites for attending the module:
- None

Prerequisites for the exam:
- Successful completion of practice and project assignments during the semester.

Letzte Änderung:
26.10.2023