Safdar Mahmood
Verfügungsgebäude 1C, Raum 1.36

T: +49 (0) 355 69 2026
F: +49 (0) 355 69 2027
safdar.mahmood(at)b-tu.de

Education
  • [2007-2011] B.Engg., PAF-KIET Karachi, Pakistan.
  • [2012-2015] M.Sc., University of Bremen, Germany.
Research Interest

AI and Machine Learning, Neural Network Topologies, AI Algorithms, Efficient Architectures for Machine Learning Acceleration, Applications of AI and ML in diverse areas including Embedded and Autonomous Systems

Publications

[2020] “A Modular Software Library for Effective High-Level Synthesis of Convolutional Neural Networks”, Muñoz Hernandez; Safdar Mahmood; Marcelo Brandalero and Michael Hübner, International Symposium on Applied Reconfigurable Computing 2020 

[2020] “Domain Adaptive Processor Architectures”, Florian Fricke; Safdar Mahmood; Javier Hoffmann; Muhammad Ali; Keyvan Shahin; Michael Hübner; Diana Göhringer, Kommunikation und Bildverarbeitung in der Automation 2020 

[2019] “IP Core Identification in FPGA Configuration Files using Machine Learning Techniques”, Safdar Mahmood ; Jens Rettkowski ; Arij Shallufa ; Michael Hübner ; Diana Göhringer, 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin) 

[2019] “Inspection of Partial Bitstreams for FPGAs Using Artificial Neural Networks”, Jens Rettkowski ; Safdar Mahmood ; Arij Shallufa ; Michael Hübner ; Diana Göhringer, 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

[2019] Flo­ri­an Fri­cke, Ja­vier Edu­ar­do Hoff­mann, Saf­dar Mah­mood, Micha­el Hüb­ner - “A Tool to Ease De­sign-Space-Ex­plo­ra­ti­on Using the Ten­si­li­ca LX7 ASIP” in CDN­Li­ve EMEA 2019 

[2018] “An Application Specific Framework for HLS-based FPGA Design of Articulated Robot Inverse Kinematics", Safdar Mahmood ; Pavel Shydlouski ; Michael Hubner, 2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig) 

This website uses cookies. There are two types of cookies: The first type supports the basic functionality of our website. The second allows us to improve our content for you by saving and analyzing pseudonymised user data. Since this second type is technically not required to run the website, you can withdraw your consent to those cookies at any time. For more information please visit our pages on data protection.

Mandatory

These cookies are needed for a smooth operation of our website.

Statistic

For statistical reasons, we use the platform Matomo to analyse the user flow with the help of website users‘ pseudonymised data. This allows us to optimize website content.

Name Purpose Lifetime Type Provider
_pk_id Used to store a few details about the user such as the unique visitor ID. 13 months HTML Matomo
_pk_ref Used to store the attribution information, the referrer initially used to visit the website. 6 months HTML Matomo
_pk_ses Short lived cookie used to temporarily store data for the visit. 30 minutes HTML Matomo