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Evaluation of Algorithms for Runtime Power Modeling of Digital Circuits

During your studies, you can contribute to our ongoing research projects. Join us in pushing the limits of what is technically feasible and be part of breaking new ground together. We offer a variety of challenging and practice-oriented topics as internships, bachelor’s or master’s theses or as student assistants (paid HiWi jobs). You will analyse important scientific preliminary questions and support the project teams with development activities.

Place of work: Erfurt

Team:

Microelectronics

Career level: 

Internship, Thesis or student job

Research field: Integrated sensor systems

Time scope:

By agreement

Start:

As soon as possible

Application deadline: 2024-12-31

Reference number:

IMMS_STUD_ME_0524

Mixed-signal ICs and ASICs that are being developed at IMMS are designed to work in scenarios where energy-efficient operation is required, e.g. in RFID sensor applications. During the development, it is particularly important to consider and optimize the energy requirements of all system components.

For digital circuits, this is usually done using special EDA software, which can be used to determine the energy consumption for a brief application scenario. However, when developing mixed-signal ICs, it is desirable to observe the energy consumption of analog and digital circuits in a joint simulation at runtime. Although this is currently possible, it is very time-consuming.

In this student project, simulation data from digital circuits is used to automatically derive abstract power models that can later be included in mixed-signal simulations. To first reduce complexity, relevant signals must be identified from a large number of time series traces. Subsequently, these relevant signals are used to infer the current energy consumption by means of regression.

WHAT TO DO:

  • Initial adaption to existing work and publications
  • Recording of simulation data using standard EDA software
  • Automatic identification of relevant signals using clustering
  • Regression between signals and power traces via machine learning
  • Implementation and documentation

WHAT TO BRING WITH YOU:

  • Motivated and independent way of working
  • Programming skills in Python
  • Interest in familiarization with circuit simulation
  • Experience with machine learning methods is an advantage

AND THIS IS US:

We strengthen enterprises with application-oriented research and development in microelectronics, systems engineering and mechatronics and transfer the results of basic research into applications and products. We support companies in launching internationally successful innovations for health, the environment and industry and provide solutions from the feasibility study to series production.

WE ARE LOOKING FORWARD TO MEETING YOU!

We thank you for your interest in working with us.

WHAT CAN WE OFFER YOU:

  • An attractive workplace in a modern, very well-equipped and industry-oriented research institute
  • Work directly at the interface between university and industry
  • Work in a flexible and creative team and on innovative and challenging topics

For the tasks described in the job offer and with the existing working conditions, an application is possible irrespective of gender and/or any physical disabilities. We foster professional equality of women and men. We invite women in particular to apply. As women are underrepresented at IMMS, they will be given priority in the case of equal suitability, ability and professional performance.

Address:

IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH (IMMS GmbH)
Ehrenbergstraße 27
98693 Ilmenau
Germany

Contact: Eric Schäfer

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