Mobile networks are used all over the world and are the corner stone for the networked society, where everything shall be connected. To support the vast amount and diversity of data expected in future networks, Ericsson are developing products to drive and support the networked society. The subjects for Master Thesis are defined to investigate and develop algorithms, architecture, tools etc. to support huge increase of speech, data and massive IoT for Radio Access Networks.
When using machine learning in resource constrained embedded systems a need arises to optimize the machine learning models, e.g. with respect to the memory used by the models, the CPU usage when training and predicting.
The thesis work is proposed to cover:
- Explore methods to optimize machine learning models, considering that the optimization step also will be performed in the embedded system itself and thus needs to be optimized and will be resource constrained.
- Investigate how these optimization methods can be implemented and deployed in a resource constrained embedded system, e.g. considering implementation language and dependencies to possible third-party components, e.g. existing open source libraries.
The thesis will be concluded with a result presentation for the Ericsson team.
This project aims at students in electrical engineering, computer science, computer engineering or similar.
1-2 students, 30hp each
Ericsson AB Mjärdevi, Linköping
Preferred Starting Date
For any questions or enquiries, please contact recruiter Sarah Lashari, firstname.lastname@example.org
Last day of application is 10th of November 2019
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Primary country and city: Sweden (SE) || || Linköping || R&D
Req ID: 302941