|Lightweight Machine Learning Systematic Literature Review|
|Organisatsiooni nimi||Software Engineering and Information Systems|
Machine learning is very popular in many applications and comprises a large variety of methods and models. Machine learning is also increasingly implemented in resource limited devices, such as embedded systems, IoT nodes, etc. Such devices have limited computational power, memory, and energy budget; at the same time, they might have to fulfill (possibly stringent) latency requirements. For such devices, lightweight (e.g. small footprint, low latency) machine learning approaches are highly desirable since they can execute “on the edge” rather than in the cloud, thereby enabling local data analytics in e.g. IoT, mobile, and automotive applications.
Objectives and tasks
The overall objective of this thesis is to conduct and document a systematic literature review of lightweight machine learning.
The tasks to be carried out include, but are not necessarily limited to:
* Survey, identify, and select relevant literature (both academic and commercial) and tools (including open-source and/or free);
* Analyze critically the selected literature and tools;
* Compare and contrast the selected literature and tools;
* Provide recommendations for selecting and implementing lightweight machine learning in real-life applications.
* An understanding of the fundamentals of machine learning
* A strong interest for lightweight machine learning
* Self-motivation and the ability to work independently
|Lõputöö kaitsmise aasta||2019-2020|
|Juhendaja||Yar Muhammad and Yannick Le Moullec|
|Märksõnad||#Lightweight Machine Learning; Embedded Systems; IoT; Computation Power; Mobile; Automotive Applications|