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Algorithm Design and Optimization of Convolutional Neural Networks Implemented on FPGAs
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]

Deep learning develops rapidly in recent years. It has been applied to many fields, which are the main areas of artificial intelligence. The combination of deep learning and embedded systems is a good direction in the technical field. This project is going to design a deep learning neural network algorithm that can be implemented on hardware, for example, FPGA. This project based on current researches about deep learning neural network and hardware features. The system uses PyTorch and CUDA as assistant methods. This project focuses on image classification based on a convolutional neural network (CNN). Many good CNN models can be studied, like ResNet, ResNeXt, and MobileNet. By applying these models to the design, an algorithm is decided with the model of MobileNet. Models are selected in some ways, like floating point operations (FLOPs), number of parameters and classification accuracy. Finally, the algorithm based on MobileNet is selected with a top-1 error of 5.5%on software with a 6-class data set.Furthermore, the hardware simulation comes on the MobileNet based algorithm. The parameters are transformed from floating point numbers to 8-bit integers. The output numbers of each individual layer are cut to fixed-bit integers to fit the hardware restriction. A number handling method is designed to simulate the number change on hardware. Based on this simulation method, the top-1 error increases to 12.3%, which is acceptable.

Abstract [sv]

Deep learning har utvecklats snabbt under den senaste tiden. Det har funnit applikationer inom många områden, som är huvudfälten inom Artificial Intelligence. Kombinationen av Deep Learning och innbyggda system är en god inriktning i det tekniska fältet. Syftet med detta projekt är att designa en Deep Learning-baserad Neural Network algoritm som kan implementeras på hårdvara, till exempel en FPGA. Projektet är baserat på modern forskning inom Deep Learning Neural Networks samt hårdvaruegenskaper.Systemet är baserat på PyTorch och CUDA. Projektets fokus är bild klassificering baserat på Convolutional Neural Networks (CNN). Det finns många bra CNN modeller att studera, t.ex. ResNet, ResNeXt och MobileNet. Genom att applicera dessa modeller till designen valdes en algoritm med MobileNetmodellen. Valet av modell är baserat på faktorer så som antal flyttalsoperationer, antal modellparametrar och klassifikationsprecision. Den mjukvarubaserade versionen av den MobileNet-baserade algoritmen har top-1 error på 5.5En hårdvarusimulering av MobileNet nätverket designades, i vilket parametrarna är konverterade från flyttal till 8-bit heltal. Talen från varje lager klipps till fixed-bit heltal för att anpassa nätverket till befintliga hårdvarubegränsningar. En metod designas för att simulera talförändringen på hårdvaran. Baserat på denna simuleringsmetod reduceras top-1 error till 12.3

Place, publisher, year, edition, pages
2019. , p. 50
Series
TRITA-EECS-EX ; 2019:219
Keywords [en]
Image Classification; Convolutional Neural Network; Top-1 Error; FPGA; Hardware Simulation
Keywords [sv]
Bild Klassificering; Konvolutionellt neuralt nätverk; Top-1 Error; FPGA; Maskinvaru Simulering
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-254575OAI: oai:DiVA.org:kth-254575DiVA, id: diva2:1333817
External cooperation
Synective Labs AB
Supervisors
Examiners
Available from: 2019-07-02 Created: 2019-07-02 Last updated: 2019-07-02Bibliographically approved

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