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TU Berlin

Inhalt des Dokuments

Daniel Maier

Contact

Contact information
Room:
E-N 638
Tel.:
+49 (0)30 314-25390
E-Mail

Office hours:
with appointment

Address:
Sekretariat EN 12
Einsteinufer 17
D-10587 Berlin

Research

  • Approximate computing
  • Software and compiler optimizations

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"

Teaching

Lehrveranstaltungen
Rechnerorganisation
WS 16/17
AES Bachelor Seminar
SS 17
Rechnerorganisation
WS 17/18
AES Bachelor Seminar
SS 18
Rechnerorganisation
WS 18/19
AES Bachelor Seminar
SS 19
Rechnerorganisation
WS 19/20
AES Bachelor Seminar
SS 20
AES Bachelor Projekt
SS 20
Seminar Recent Advances in Computer Architecture
WS 20/21
AES Bachelor Projekt
SS 21
Seminar Recent Advances in Computer Architecture
WS 21/22
AES Bachelor Projekt
SS 22
Seminar Recent Advances in Computer Architecture
WS 22/23

Supervised Theses

Betreute Abschlussarbeiten
Titel
Name
Memory-aware Weight Pruning for Deep Neural Networks
Thomas Hartenstein (results published)
Automatic Transformations for Local Memory Usage in OpenCL Kernels
Stefan Gebauer
Automation of the optical beamline and integration with the network
Keerthana Ranganathan (Helmholtz-Zentrum Berlin)
Effects of Approximate Computing on Workload Character
Stefan Schirmeister (results published)
The Impact of Reconstruction in Approximate Computing
Jahn Michel
Hardware Software Co−Design of Neural Network
Accelerators
Muhammad Usama Javed (Bosch)

Publications

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.

Link to publication

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.


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


Maier, Daniel and Juurlink, Ben (2022). Model-Based Loop Perforation. Euro-Par 2021: Parallel Processing Workshops. Springer International Publishing, 549–554.


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.

Link to publication

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)



Hartenstein, Thomas AND Maier, Daniel AND Cosenza, Biagio AND Juurlink, Ben (2020). Weight Pruning for Deep Neural Networks on GPUs. PARS-Mitteilungen, 51-62.


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


Zusatzinformationen / Extras

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