Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/D10A98E6-AFA7-499F-BB74-CC4B5A02BE95 | ||
| Year | 2024 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Sotirios Michail, "Study of a rotationally invariant hardware implementable convolutional neural network using CORDIC arithmetic", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2024 https://doi.org/10.26233/heallink.tuc.98959 | ||
| Appears in Collections |
Introduced in this thesis is an approach in enhancing the rotational invariance of Convolutional Neural Networks (CNNs), through integrating the novel Log-CORDIC algorithm for image pre-processing. This image pre-processing algorithm presents an advantage over existing cartesian-to-polar transform algorithms for images, through the computational advantages of the Coordinate Rotation Digital Computer (CORDIC) algorithm. The results of the novel algorithm are studied and compared with existing transform methods, along with its efficiency improvements, and its ability to enhance rotational invariance in a CNN is ascertained by integrating it into the pipeline of a customized SqueezeNet neural network. Focusing on the CIFAR-10 and MNIST datasets, experiments with this customized SqueezeNet neural network demonstrate an improvement in classification accuracy for images with varied orientations.
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