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Tag: Edge Computing

Profile picture of Hans Jakob Damsgaard. Forest and lake in the background. Photo by: Anna Gaydamaka
12.12.2024

Hans Jakob Damsgaard: Making mistakes can extend your phone’s battery life

Many modern applications based on machine learning, multimedia, or signal processing can tolerate small computational errors. Approximate Computing (AxC) techniques deliberately exploit this tolerance to save power, latency, circuit area, or other system metrics. M.Sc. Hans Jakob Damsgaard’s doctoral dissertation explores the integration of AxC into adaptive hardware architectures.

  • Doctoral dissertation
Profile picture for Waleed Bin Qaim. Photo: Rabia Qadar
15.4.2024

Waleed Bin Qaim: Improving performance of Smart Wearable Devices through Edge Computing and Machine Learning

As consumer demand for more smarter and capable wearable gadgets grows, wearable technology faces challenges like short battery life and insufficient computing power to handle complex tasks. In his doctoral dissertation, M.Sc. Waleed Bin Qaim proposes a novel Machine Learning-based framework focussed on computational task offloading to address these challenges. His research allows wearables to leverage edge computing resources, enhancing both performance and user experience.

  • Doctoral dissertation

Contact persons

Mikko Valkama, Professor
mikko.valkama@tuni.fi

Joonas Säe, Staff Scientist
joonas.sae@tuni.fi

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