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TU Berlin

Inhalt des Dokuments

Daniel Maier

Kontaktdaten

Kontaktdaten
Raum:
E-N 638
Tel.:
+49 (0)30 314-25390
E-Mail

Sprechstunde:
nach Vereinbarung

Anschrift:
Sekretariat EN 12
Einsteinufer 17
D-10587 Berlin

Abschlussarbeiten

  • currently no open projects available

Forschung

  • Approximate computing
  • Software- und Compiler-Optimierungen

Awards

  • Outstanding Paper Award Runner Up auf der International Conference on High Performance Computing & Simulation (HPCS 2019) für das Paper "Approximating Memory-bound Applications on Mobile GPUs"
  • Best Paper Award auf der Eighth EAI International Conference on Simulation Tools and Techniques (SIMUTools 2015) für das Paper "Deterministic Models of the Physical Layer through Signal Simulation"

Lehre

Lehrveranstaltungen
Rechnerorganisation
WS 16/17
AES Bachelor Seminar
SS 17
Rechnerorganisation
WS 17/18
AES Bachelor Seminar
SS 18

Publikationen


Daniel Maier and Steffen Moser and Frank Slomka (2015). Deterministic Models of the Physical Layer Through Signal Simulation. Proceedings of the 8th International Conference on Simulation Tools and Techniques. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 175–182.


Daniel Maier and Biagio Cosenza and Ben Juurlink (2018). Local Memory-Aware Kernel Perforation. Proceedings of the 2018 International Symposium on Code Generation and Optimization. ACM.


Daniel Maier, Nadjib Mammeri, Biagio Cosenza, Ben Juurlink (2019). Approximating Memory-bound Applications on Mobile GPUs. 2019 International Conference on High Performance Computing & Simulation (HPCS)


Mohammad Loni and Ali Zoljodi and Daniel Maier and Amin Majd and Masoud Daneshtalab and Mikael Sjödin and Ben Juurlink and Reza Akbari (2020). DenseDisp: Resource-Aware Disparity Map Estimation by Compressing Siamese Neural Architecture. IEEE World Congress On Computational Intelligence (WCCI) 2020


Daniel Maier and Biagio Cosenza and Ben Juurlink (2021). ALONA: Automatic Loop Nest Approximation with Reconstruction and Space Pruning. Euro-Par 2021: Parallel Processing. Springer International Publishing, 3–18.


Thomas Hartenstein and Daniel Maier and Biagio Cosenza and Ben Juurlink (2019,). Memory-aware Weight Pruning for Deep Neural Networks.. PARS-Mitteilungen, (to appear)


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