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Resource Efficient Representation of Machine Learning Models: investigating optimization options for decision trees in embedded systems
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Resurseffektiv Representation av Maskininlärningsmodeller (Swedish)
Abstract [en]

Combining embedded systems and machine learning models is an exciting prospect. However, to fully target any embedded system, with the most stringent resource requirements, the models have to be designed with care not to overwhelm it.

Decision tree ensembles are targeted in this thesis. A benchmark model is created with LightGBM, a popular framework for gradient boosted decision trees. This model is first transformed and regularized with RuleFit, a LASSO regression framework. Then it is further optimized with quantization and weight sharing, techniques used when compressing neural networks. The entire process is combined into a novel framework, called ESRule. The data used comes from the domain of frequency measurements in cellular networks. There is a clear use-case where embedded systems can use the produced resource optimized models.

Compared with LightGBM, ESRule uses 72ˆ less internal memory on average, simultaneously increasing predictive performance. The models use 4 kilobytes on average. The serialized variant of ESRule uses 104ˆ less hard disk space than LightGBM. ESRule is also clearly faster at predicting a single sample.

Place, publisher, year, edition, pages
2019. , p. 43
Keywords [en]
machine learning, rule fit, decision trees, embedded systems, resources, ensemble methods, lasso, regression, optimization
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-162013ISRN: LIU-IDA/LITH-EX-A--2019/064--SEOAI: oai:DiVA.org:liu-162013DiVA, id: diva2:1370711
External cooperation
Ericsson AB
Subject / course
Computer Engineering
Presentation
2019-06-18, John von Neumann, 13:15 (English)
Supervisors
Examiners
Available from: 2019-11-20 Created: 2019-11-17 Last updated: 2019-11-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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