Bachelor and Master Theses

Level: Advanced
Description: In later years the interest for deep networks and convolutional networks in regards to object recog- nition has spiked. There are however not many that focuses on the hardware in these subjects. This thesis was done in collaboration with Unibap to explore the feasibility of implementing these on a FPGA to speed up object recognition. A custom database is created to investigate if a smaller database could be utilized with good results. This database alongside the MNIST database are tested in multiple configurations to find a suitable solution with good enough accuracy. This thesis fo- cuses on getting an accuracy which could be applicable in industries of today and is therefore not as driven by accuracy as many other works. Furthermore a FPGA implementation that is ver- satile and flexible enough to utilize regardless of network configuration is built and simulated. To achieve this research was done on existing AI and the focus landed on convolutional neural net- works. The different configurations are all presented in regards to time, resource utilization and accuracy. The FPGA implementation in this work is only simulated and this leaves the desire and need to syntethize it on an actual FPGA.
Company: Unibap, kontaktperson: Lars Asplund
Prel. end date: 2017-05-17
Presentation date: 2017-06-12
Student: Daniel Jonasson
IDT supervisor: Ning Xiong, +46-21-151716
Examinator: Masoud Daneshtalab
Masoud Daneshtalab

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2017-05-17, 21:52

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