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An Embedded System for Classification and Dirt Detection on Surgical Instruments
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The need for automation in healthcare has been rising steadily in recent years, both to increase efficiency and for freeing educated workers from repetitive, menial, or even dangerous tasks. This thesis investigates the implementation of two pre-determined and pre-trained convolutional neural networks on an FPGA for the classification and dirt detection of surgical instruments in a robotics application. A good background on the inner workings and history of artificial neural networks is given and expanded on in the context of convolutional neural networks. The Winograd algorithm for computing convolutional operations is presented as a method for increasing the computational performance of convolutional neural networks. A selection of development platform and toolchains is then made. A high-level design of the overall system is explained, before details of the high-level synthesis implementation of the dirt detection convolutional neural network are shown. Measurements are then made on the performance of the high-level synthesis implementation of the various blocks needed for convolutional neural networks. The main convolutional kernel is implemented both by using the Winograd algorithm and the naive convolution algorithm and comparisons are made. Finally, measurements on the overall performance of the end-to-end system are made and conclusions are drawn. The final product of the project gives a good basis for further work in implementing a complete system to handle this functionality in a manner that is both efficient in power and low in latency. Such a system would utilize the different strengths of general-purpose sequential processing and the parallelism of an FPGA and tie those together in a single system.

Abstract [sv]

Behovet av automatisering inom vård och omsorg har blivit allt större de senaste åren, både vad gäller effektivitet samt att befria utbildade arbetare från repetitiva, enkla eller till och med farliga arbetsmoment. Den här rapporten undersöker implementeringen av två tidigare för-definierade och för-tränade faltade neurala nätverk på en FPGA, för att klassificera och upptäcka föroreningar på kirurgiska verktyg. En bra bakgrund på hur neurala nätverk fungerar, och deras historia, presenteras i kontexten faltade neurala nätverk. Winograd algoritmen, som används för att beräkna faltningar, beskrivs som en metod med syfte att öka beräkningsmässig prestanda. Val av utvecklingsplattform och verktyg utförs. Systemet beskrivs på en hög nivå, innan detaljer om hög-nivå-syntesimplementeringen av förorenings-detekterings-nätverket visas. Mätningar görs sedan av de olika bygg-blockens prestanda. Kärnkoden med faltnings-algoritmen implementeras både med Winograd-algoritmen och med den traditionella, naiva, metoden, och utfallet för bägge metoderna jämförs. Slutligen utförs mätningar på hela systemets prestanda och slutsatser dras därav. Projektets slutprodukt kan användas som en bra bas för vidare utveckling av ett komplett system som både är effektivt angående effektförbrukning och har bra prestanda, genom att knyta ihop styrkan hos traditionella sekventiella processorer med parallelismen i en FPGA till ett enda system.

Place, publisher, year, edition, pages
2019. , p. 54
Series
TRITA-EECS-EX ; 2019:805
Keywords [en]
Neural Network, CNN, FPGA, PetaLinux, Winograd, High-level Synthesis
Keywords [sv]
Neuralt nätverk, Faltade neurala nätverk, FPGA, PetaLinux, Winograd, Hög-nivå syntes
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-271195OAI: oai:DiVA.org:kth-271195DiVA, id: diva2:1415977
Subject / course
Electrical Engineering
Supervisors
Examiners
Available from: 2020-03-20 Created: 2020-03-20 Last updated: 2020-03-20Bibliographically approved

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Electrical Engineering, Electronic Engineering, Information Engineering

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4344454647484946 of 204
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