TU Berlin

Embedded Systems ArchitectureMaier, Daniel

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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

Master/Bachelor Projects

  • currently no open projects available

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"

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.


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


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)



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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