Publications of M.Sc Mikail Yayla
2023
- HW/SW Codesign for Approximation-Aware Binary Neural Networks
Abhilasha Dave, Fabio Frustaci, Fanny Spagnolo, Mikail Yayla, Jian-Jia Chen, Hussam Amrouch
IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS, accepted) - Robust and Efficient Machine Learning for Emerging Resource-Constrained Embedded Systems (abstract, poster)
Mikail Yayla, Jian-Jia Chen
Poster Presentation for PhD Forum, DATE'23 - Global by Local Thresholding in Binarized Neural Networks for Efficient Crossbar Accelerator Design.
Mikail Yayla, Fabio Frustaci, Fanny Spagnolo, Jian-Jia Chen, Hussam Amrouch
(under review) - HEP-BNN: A Framework for Finding Low-Latency Execution Configurations of BNNs on Heterogenerous Multiprocesser Platforms.
Leonard David Bereholschi, Ching-Chi Lin, Mikail Yayla, Jian-Jia Chen
AccML@HiPEAC'23: 5th Workshop on Accelerated Machine Learning
2022
- TREAM: A Tool for Evaluating Error Resilience of Tree-based Models using Approximate Memory.
Mikail Yayla, Zahra Valipour Dehnoo, Mojtaba Masoudinejad, and Jian-Jia Chen
SAMOS XXII International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation - Memory-Efficient Training of Binarized Neural Networks on the Edge.
Mikail Yayla and Jian-Jia Chen
Design Automation Conference (DAC'22) - Reliable Binarized Neural Networks on Unreliable Beyond von-Neumann Architecture.
Mikail Yayla, Simon Thomann, Sebastian Buschjäger, Katharina Morik, Jian-Jia Chen, and Hussam Amrouch
IEEE Transactions on Curcuits and Systems I (TCAS-I) - Deep Learning Based Driver Model and Fault Detection for Automated Racecar System Testing.
Yousef Abdulhamed, Christoph Schaefer, Mikail Yayla, Ching-Chi Lin, Jian-Jia Chen
European Automotive Reliability, Test and Safety (eARTS), DATE'22 Workshop
2021
- FeFET-based Binarized Neural Networks Under Temperature-dependent Bit Errors.
Mikail Yayla, Sebastian Buschjäger, Aniket Gupta, Jian-Jia Chen, Jörg Henkel, Katharina Morik, Kuan-Hsun Chen, Hussam Amrouch.
IEEE Transactions on Computers (TC) - Binarized SNNs: Efficient and Error-Resilient Spiking Neural Networks through Binarization.
Ming-Liang Wei, Mikail Yayla, Shu-Yin Ho, Jian-Jia Chen, Chia-Lin Yang, Hussam Amrouch.
International Conference On Computer Aided Design (ICCAD'21) - Universal Approximation Theorems for Fully Connected Binarized Neural Networks.
Mikail Yayla, Mario Günzel, Burim Ramosaj, Jian-Jia Chen.
CoRR abs/2102.02631 - Bit Error Tolerance Metrics for Binarized Neural Networks.
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen, Mario Günzel, Christian Hakert, Katharina Morik, Rodion Novkin, Lukas Pfahler and Mikail Yayla.
SLOHA (DATE'21 Workshop) - FeFET and NCFET for Future Neural Networks: Visions and Opportunities.
Mikail Yayla, Kuan-Hsun Chen, Georgios Zervakis, Jörg Henkel, Jian-Jia Chen and Hussam Amrouch.
In Design, Automation and Test in Europe Conference (DATE'21), Special Session - Margin-Maximization in Binarized Neural Networks for Optimizing Bit Error Tolerance.
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen, Mario Günzel, Christian Hakert, Katharina Morik, Rodion Novkin, Lukas Pfahler and Mikail Yayla.
In Design, Automation and Test in Europe Conference (DATE'21), Best Paper Candidate
2020
- Software-Based Memory Analysis Environments for In-Memory Wear-Leveling.
Christian Hakert, Kuan-Hsun Chen, Mikail Yayla, Georg von der Brüggen, Sebastian Bloemeke and Jian-Jia Chen.
In 25th Asia and South Pacific Design Automation Conference ASP-DAC 2020, Invited Paper - Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks.
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen, Mario Günzel, Christian Hakert, Katharina Morik, Rodion Novkin, Lukas Pfahler and Mikail Yayla.
CoRR abs/2002.00909
2019
- Stack Usage Analysis for Efficient Wear Leveling in Non-Volatile Main Memory Systems.
Christian Hakert, Mikail Yayla, Kuan-Hsun Chen, Georg von der Brüggen, Jian-Jia Chen, Sebastian Buschjäger, Katharina Morik, Paul R. Genssler, Lars Bauer, Hussam Amrouch and Jörg Henkel.
In 1st ACM/IEEE Workshop on Machine Learning for CAD (MLCAD) - Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms.
Mikail Yayla, Anas Toma, Kuan-Hsun Chen, Lenssen, Victoria Shpacovitch, Roland Hergenröder, Frank Weichert and Jian-Jia Chen.
Journal of Sensors 19 4318 - Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis.
Mikail Yayla, Anas Toma, Jan Eric Lenssen, Victoria Shpacovitch, Kuan-Hsun Chen, Frank Weichert and Jian-Jia Chen.
In BVM'19 Workshop
2018
- Fault Tolerance on Control Applications: Empirical Investigations of Impacts from Incorrect Calculations.
Mikail Yayla, Kuan-Hsun Chen and Jian-Jia Chen.
In 4th Workshop on Emerging Ideas and Trends in Engineering of Cyber-Physical Systems (EITEC'18@CPSWEEK)
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Location & approach
The campus of TU Dortmund University is located close to interstate junction Dortmund West, where the Sauerlandlinie A 45 (Frankfurt-Dortmund) crosses the Ruhrschnellweg B 1 / A 40. The best interstate exit to take from A 45 is "Dortmund-Eichlinghofen" (closer to Campus Süd), and from B 1 / A 40 "Dortmund-Dorstfeld" (closer to Campus Nord). Signs for the university are located at both exits. Also, there is a new exit before you pass over the B 1-bridge leading into Dortmund.
To get from Campus Nord to Campus Süd by car, there is the connection via Vogelpothsweg/Baroper Straße. We recommend you leave your car on one of the parking lots at Campus Nord and use the H-Bahn (suspended monorail system), which conveniently connects the two campuses.
TU Dortmund University has its own train station ("Dortmund Universität"). From there, suburban trains (S-Bahn) leave for Dortmund main station ("Dortmund Hauptbahnhof") and Düsseldorf main station via the "Düsseldorf Airport Train Station" (take S-Bahn number 1, which leaves every 20 or 30 minutes). The university is easily reached from Bochum, Essen, Mülheim an der Ruhr and Duisburg.
You can also take the bus or subway train from Dortmund city to the university: From Dortmund main station, you can take any train bound for the Station "Stadtgarten", usually lines U41, U45, U 47 and U49. At "Stadtgarten" you switch trains and get on line U42 towards "Hombruch". Look out for the Station "An der Palmweide". From the bus stop just across the road, busses bound for TU Dortmund University leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dortmund main station to the stop "Dortmund Kampstraße". From there, take U43 or U44 to the stop "Dortmund Wittener Straße". Switch to bus line 447 and get off at "Dortmund Universität S".
The AirportExpress is a fast and convenient means of transport from Dortmund Airport (DTM) to Dortmund Central Station, taking you there in little more than 20 minutes. From Dortmund Central Station, you can continue to the university campus by interurban railway (S-Bahn). A larger range of international flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the university station.
The H-Bahn is one of the hallmarks of TU Dortmund University. There are two stations on Campus Nord. One ("Dortmund Universität S") is directly located at the suburban train stop, which connects the university directly with the city of Dortmund and the rest of the Ruhr Area. Also from this station, there are connections to the "Technologiepark" and (via Campus Süd) Eichlinghofen. The other station is located at the dining hall at Campus Nord and offers a direct connection to Campus Süd every five minutes.
The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent "Technologiepark".
Site Map of TU Dortmund University (Second Page in English).