Profil

Belabed Tarek

Main Referenced Co-authors
VALDERRAMA SAKUYAMA, Carlos Alberto  (9)
SOUANI, Chokri (7)
Ramos gomes da silva, Vitor  (5)
QUENON, Alexandre  (3)
Maria Gracielly, F. Coutinho (2)
Main Referenced Keywords
FPGA (4); Cloud Computing (3); Edge Computing (3); DNN (2); framework (2);
Main Referenced Unit & Research Centers
CRTI - Centre de Recherche en Technologie de l'Information (4)
Main Referenced Disciplines
Computer science (10)
Electrical & electronics engineering (10)
Physics (4)
Library & information sciences (4)

Publications (total 10)

The most downloaded
17 downloads
Belabed, T., Maria Gracielly, F. C., FERNANDES, M. A. C., Valderrama, C., & SOUANI, C. (2021). User Driven FPGA-Based Design Automated Framework of Deep Neural Networks for Low-Power Low-Cost Edge Computing. IEEE Access. https://hdl.handle.net/20.500.12907/42420

The most cited

13 citations (Scopus®)

Belabed, T., Maria Gracielly, F. C., FERNANDES, M. A. C., Valderrama, C., & SOUANI, C. (2021). User Driven FPGA-Based Design Automated Framework of Deep Neural Networks for Low-Power Low-Cost Edge Computing. IEEE Access. https://hdl.handle.net/20.500.12907/42420

BOUGUEZZI, S., BEN FREDJ, H., Belabed, T., Valderrama, C., Faiedh, H., & SOUANI, C. (16 September 2021). An Efficient FPGA-Based Convolutional Neural Network forClassification: Ad-MobileNet. Electronics, 10 (18), 2272. doi:https://doi.org/10.3390/electronics10182272
Peer reviewed

Belabed, T., Ramos Gomes Da Silva, V.* , Quenon, A.* , Valderrama, C., & SOUANI, C. (2021). A Novel Automate Python Edge-to-Edge: From Automate Generation On Cloud To User Application Deployment on Edge of Deep Neural Networks For Low Power IoT Systems FPGA-Based Acceleration. Sensors. doi:10.3390/s21186050
Peer Reviewed verified by ORBi
* These authors have contributed equally to this work.

Belabed, T., Quenon, A., Ramos Gomes Da Silva, V., Valderrama, C., & SOUANI, C. (2021). Full Python Interface Control: Auto Generation And Adaptation of Deep Neural Networks For Edge Computing and IoT Applications FPGA-Based Acceleration. In Proceedings of 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE. doi:10.1109/INISTA52262.2021.9548521
Peer reviewed

Belabed, T., Quenon, A., Ramos Gomes Da Silva, V., Valderrama, C., & SOUANI, C. (26 August 2021). Full Python Interface Control: Auto Generation And Adaptation of Deep Neural Networks For Edge Computing and IoT Applications FPGA-Based Acceleration [Paper presentation]. International Symposium on INnovations in Intelligent SysTems and Applications, Kocaeli, Turkey.
Peer reviewed

Belabed, T., Maria Gracielly, F. C., FERNANDES, M. A. C., Valderrama, C., & SOUANI, C. (2021). User Driven FPGA-Based Design Automated Framework of Deep Neural Networks for Low-Power Low-Cost Edge Computing. IEEE Access.
Peer Reviewed verified by ORBi

Belabed, T., Maria Gracielly, F. C., Fernandes, M. A. C., Valderrama, C., & SOUANI, C. (20 October 2020). Low Cost and Low Power Stacked Sparse Autoencoder Hardware Acceleration for Deep Learning Edge Computing Applications [Paper presentation]. 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia.

Yvanoff-Frenchin, C., Ramos Gomes Da Silva, V., Belabed, T., & Valderrama, C. (29 February 2020). Edge Computing Robot Interface for Automatic Elderly Mental Health Care Based on Voice. MDPI, 9 (3), 419. doi:10.3390/electronics9030419
Peer reviewed

Belabed, T., & Valderrama, C. (02 December 2019). Autoencoder hardware topologies for Edge Computing [Poster presentation]. MUSICS-FNRS doctoral school 6th Reconfigurable Market Workshop, Mons, Belgium.

Yvanoff-Frenchin, C., Ramos Gomes Da Silva, V., Belabed, T., & Valderrama, C. (2019). An Edge Computing Robot Experience for Automatic Elderly Mental Health Care Based on Voice [Paper presentation]. ICT Innovations, Ohrid, North Macedonia.

Belabed, T., Jemmali, S., & SOUANI, C. (24 May 2018). FFT implementation and optimization on FPGA [Paper presentation]. 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia. doi:10.1109/ATSIP.2018.8364454

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