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Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/D2FD3227-CA1F-4EF7-9FB3-9BA5A04CA1CE | - |
| Identifier | https://doi.org/10.26233/heallink.tuc.82994 | - |
| Language | en | - |
| Extent | 89 pages | en |
| Title | Performance landscape of CNN acceleration tools and resource constrained platforms | el |
| Title | Αξιολόγηση της απόδοσης των εργαλείων επιτάχυνσης των συνελικτικών νευρωνικών δικτύων και των πλατφορμών περιορισμένων πόρων | el |
| Creator | Miliadis Panagiotis | en |
| Creator | Μηλιαδης Παναγιωτης | el |
| Contributor [Thesis Supervisor] | Pnevmatikatos Dionysios | en |
| Contributor [Thesis Supervisor] | Πνευματικατος Διονυσιος | el |
| Contributor [Committee Member] | Dollas Apostolos | en |
| Contributor [Committee Member] | Δολλας Αποστολος | el |
| Contributor [Committee Member] | Theodoropoulos Dimitrios | en |
| Contributor [Committee Member] | Θεοδωροπουλος Δημητριος | el |
| Publisher | Πολυτεχνείο Κρήτης | el |
| Publisher | Technical University of Crete | en |
| Academic Unit | Technical University of Crete::School of Electrical and Computer Engineering | en |
| Academic Unit | Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών | el |
| Content Summary | Over the last years, a rapid growth in the development of applications that are based on Convolutional Neural Networks is observed. Despite of the large advances in processor units, the use of computer vision tasks is still challenging in resource constrained platforms. This thesis will present four toolkits, that accelerate the performance of inference applications by targeting the processor units from the top hardware vendors; Intel, Nvidia, Arm and Xilinx. In order to achieve optimal execution, the toolkits exploit the hardware acceleration that processors provide, as well as special processor units and platforms, which are specially developed for deep learning inference tasks. The most well-known models for each task are described, alongside with the frameworks that the toolkits support and are used for model representation. Last but not least, real-world performance results are collected for different batches of images, in order to achieve a performance landscape of the existing tools. | en |
| Type of Item | Διπλωματική Εργασία | el |
| Type of Item | Diploma Work | en |
| License | http://creativecommons.org/licenses/by-nc/4.0/ | en |
| Date of Item | 2019-09-02 | - |
| Date of Publication | 2019 | - |
| Subject | CNN | en |
| Subject | Acceleration | en |
| Subject | Acceleration tools | en |
| Bibliographic Citation | Panagiotis Miliadis, "Performance landscape of CNN acceleration tools and resource constrained platforms", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2019 | en |
| Bibliographic Citation | Παναγιώτης Μηλιάδης, "Αξιολόγηση της απόδοσης των εργαλείων επιτάχυνσης των συνελικτικών νευρωνικών δικτύων και των πλατφορμών περιορισμένων πόρων", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2019 | el |
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