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Implementation and evaluation of selected Machine Learning algorithms on a resource constrained telecom hardware platform
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Implementation och utvärdering av utvalda maskininlärningsalgoritmer på en resursbegränsad telekom-maskinvaruplattform (Swedish)
Abstract [en]

The vast majority of computing hardware platforms available today are not desktop PCs. They are embedded systems, sensors and small specialized pieces of hardware present in almost every digital product available today. Due to the massive amount of information available through these devices we can find new and exciting ways to apply and benefit from machine learning. Many of these computing devices have specialized, resource-constrained architectures and it might be problematic to perform complicated computations. If such a system is under heavy load or has restricted performance, computational power is a valuable resource and costly algorithms must be avoided. \\This master thesis will present an in-depth study investigating the trade-offs between precision, latency and memory consumption of a selected set of machine learning algorithms implemented on a resource constrained multi-core telecom hardware platform. This report includes motivations for the selected algorithms, discusses the results of the algorithms execution on the hardware platform and offers conclusions relevant to further developments.

Abstract [sv]

Majoriteten av beräkningsplattformarna som finns tillgängliga idag är inte stationära bordsdatorer. De är inbyggda system, sensorer och små specialiserade hårdvaror som finns i nästan alla digitala produkter tillgängliga idag. På grund av den enorma mängden information som finns tillgänglig via dessa enheter kan vi hitta nya och spännande sätt att dra nytta av maskininlärning. Många av dessa datorer har specialiserade, resursbegränsade arkitekturer och det kan vara problematiskt att utföra de komplicerade beräkningar som behövs. Om ett sådant system är tungt belastat eller har begränsad prestanda, är beräkningskraft en värdefull resurs och kostsamma algoritmer måste undvikas. \\ Detta masterprojekt kommer att presentera en djupgående studie som undersöker avvägningarna mellan precision, latens och minneskonsumtion av en utvald uppsättning maskininlärningsalgoritmer implementerade på en resursbegränsad flerkärnig telekom-maskinvaruplattform. Denna rapport innehåller motivationer för de valda algoritmerna, diskuterar resultaten av algoritmerna på hårdvaruplattformen och presenterar slutsatser som är relevanta för vidareutveckling.

Place, publisher, year, edition, pages
2017.
Keywords [sv]
Maskininlärning, Resursbegränsade system, Parallelism, Telecom, 5g
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-214628OAI: oai:DiVA.org:kth-214628DiVA, id: diva2:1142220
External cooperation
Ericsson
Subject / course
Computer Science
Educational program
Master of Science - Computer Science
Supervisors
Examiners
Available from: 2017-10-16 Created: 2017-09-18 Last updated: 2018-01-13Bibliographically approved

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