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M.Sc. Thesis Project: Unlocking the Future of Energy-Efficient IoT

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Website RISE Research Institute of Sweden

We have an open position for a dedicated master student to join us at the Connected Intelligence research unit, within the Computer Science department at RISE.

Background and Purpose
WebAssembly (WASM) is a portable binary instruction format that enables efficient and secure execution of code on web browsers and beyond. Its implementation on IoT (Internet of Things) devices is gaining traction due to its lightweight footprint and near-native performance. By running WASM modules on IoT devices, developers can harness the power of a wide range of programming languages, enhancing flexibility and adaptability in the IoT ecosystem. This approach allows for efficient, low-latency processing, making it well-suited for resource-constrained devices, enabling real-time data analysis, and enabling seamless integration with web-based services. As IoT continues to grow, the adoption of WASM on these devices promises to revolutionise the way we collect, process, and utilise data in the connected world.

Thesis Description
The thesis will focus on investigating the footprint of machine learning inference on constrained devices for IoT. After a thorough literature review of related research works, a series of scientific experiments will be defined to measure performance, power consumption, memory and computing resources used when inferring with WASM and pre-trained machine learning models. A list of appropriate devices, data input and models will be defined and experiments will be implemented to compare inference performance and footprint with and without WASM.

Terms:

  • Start time: As soon as possible
  • Scope: 30 hp (20 weeks)
  • Location: RISE Computer Science, Stockholm (Kista). Option to partially work remotely.

Who are you?
We expect you to have good programming skills, experience in applying machine learning[BA1] models, and an interest/curiosity in IoT and embedded systems.

Welcome with your application!
To know more, please contact Dr Fehmi Ben Abdesslem ([email protected]) and Joakim Eriksson ([email protected]). Applications should include a brief personal letter, CV, recent grades, and a code example. Candidates are encouraged to send in their application as soon as possible but at the latest by the 15th of January 2024. Suitable applicants will be interviewed as soon as applications are received. All applications go through our recruitment tool Varbi; we do not handle applications via email.

Master thesis, Machine Learning, IoT, WASM, Energy Efficiency, RISE, Stockholm

Om jobbet

Ort

Kista

Anställningsform

Särskild visstidsanställning

Job type

Student – examensarbete/praktik

Kontaktperson

Joakim Eriksson
+46102284364

Referensnummer

2023/583

Sista ansökningsdag

2024-01-15

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Website RISE Research Institute of Sweden

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