Performance landscape of CNN acceleration tools and resource constrained platformsPerformance landscape of CNN acceleration tools and resource constrained platformsΑξιολόγηση της απόδοσης των εργαλείων επιτάχυνσης των συνελικτικών νευρωνικών δικτύων και των πλατφορμών περιορισμένων πόρων Διπλωματική Εργασία Diploma Work 2019-09-022019enOver 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.http://creativecommons.org/licenses/by-nc/4.0/Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών ΥπολογιστώνMiliadis_Panagiotis_Dip_2019.pdfChania [Greece]Library of TUC2019-09-02application/pdf9.6 MBfree Miliadis Panagiotis Μηλιαδης Παναγιωτης Pnevmatikatos Dionysios Πνευματικατος Διονυσιος Dollas Apostolos Δολλας Αποστολος Theodoropoulos Dimitrios Θεοδωροπουλος Δημητριος Πολυτεχνείο Κρήτης Technical University of Crete CNN Acceleration Acceleration tools