Dimitrios Danopoulos is a PhD canditate at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA). His research area involves the development and hardware acceleration of Machine Learning applications in the cloud for data centers. Also, he has received his electrical and computer engineering diploma degree at NTUA. During his studies, he majored in sciences that mainly concerned software and computing systems, but bioengineering as well. His diploma thesis, which involved the development of a Deep-Learning application for Image Recognition, was successfully published at the IEEE and was presented at the MOCAST conference. In particular, it was applied to the Caffe environment and was succesfully accelerated using hardware accelerators, resulting in rapid recognition of RGB images on low-power FPGA platforms. After his diploma thesis, he continued his research on Cloud Computing, Computer Architecture and Deep Learning such as neural networks etc. The practical skills he gained through research in this field helped him to contribute to the project of Vineyard H2020 in which he participated to the research team. His competence in computers, however, was evident at an early stage as at the age of 16 he managed to achieve a high distinction in the Pan-Hellenic IT Competition.
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Approximate similarity search with FAISS framework using FPGAs on the cloud
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, DOI: 10.1007/978-3-030-27562-4_27
Journals
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Using LSTM Neural Networks as Resource Utilization Predictors: The Case of Training Deep Learning Models on the Edge
International Conference on the Economics of Grids, Clouds, Systems, and Services, DOI: https://doi.org/10.1007/978-3-030-63058-4_6 -
Acceleration of image classification with Caffe framework using FPGA
2018 7th International Conference on Modern Circuits and Systems Technologies, MOCAST 2018 DOI: 10.1109/MOCAST.2018.8376580 -
Automatic Generation of FPGA Kernels from Open Format CNN Models
Proceedings - 28th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2020, DOI: 10.1109/FCCM48280.2020.00070
Conferences
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Hardware Acceleration methods for the latest Deep Learning applications using GANs
Academic Advisors: Dimitrios Soudris and Dimitris DanopoulosBrief: PDF -
Improvement of sky-imaging PV production forecasts with application of deep learning
Academic Advisors: Dimitrios Soudris , Dimitrios Anagnostos , Dimitris Danopoulos and Ioannis OroutzoglouBrief: PDF
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Hardware Acceleration methods for the latest Deep Learning applications using GANs
Academic Advisors: Dimitrios Soudris and Dimitris DanopoulosBrief: PDF -
Improvement of sky-imaging PV production forecasts with application of deep learning
Academic Advisors: Dimitrios Soudris , Dimitrios Anagnostos , Dimitris Danopoulos and Ioannis OroutzoglouBrief: PDF