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A Digits-Recognition Convolutional Neural Network on FPGA
Linköping University, Department of Electrical Engineering, Computer Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Ett faltningsbaserat neuralt nätverk för sifferigenkänning på FPGA (Swedish)
Abstract [en]

A convolutional neural network (CNN) is a deep learning framework that is widely used in computer vision. A CNN extracts important features of input images by perform- ing convolution and reduces the parameters in the network by applying pooling operation. CNNs are usually implemented with programming languages and run on central process- ing units (CPUs) and graphics processing units (GPUs). However in recent years, research has been conducted to implement CNNs on field-programmable gate array (FPGA).

The objective of this thesis is to implement a CNN on an FPGA with few hardware resources and low power consumption. The CNN we implement is for digits recognition. The input of this CNN is an image of a single digit. The CNN makes inference on what number it is on that image. The performance and power consumption of the FPGA is compared with that of a CPU and a GPU.

The results show that our FPGA implementation has better performance than the CPU and the GPU, with respect to runtime, power consumption, and power efficiency.

Place, publisher, year, edition, pages
2019. , p. 37
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:liu:diva-161663ISRN: LiTH-ISY-EX--19/5264--SEOAI: oai:DiVA.org:liu-161663DiVA, id: diva2:1367984
Subject / course
Electrical Engineering
Presentation
2019-10-08, Transformen, Linkoping University, Linkoping, 11:00 (English)
Supervisors
Examiners
Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-05Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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