no code implementations • 22 Jun 2023 • Patrick Plagwitz, Frank Hannig, Jürgen Teich, Oliver Keszocze
Due to their compute- and data-intensive nature, CNN accelerators have been developed as ASICs or on FPGAs.
no code implementations • 23 May 2023 • Mark Deutel, Georgios Kontes, Christopher Mutschler, Jürgen Teich
Deploying Deep Neural Networks (DNNs) on tiny devices is a common trend to process the increasing amount of sensor data being generated.
no code implementations • 20 May 2022 • Mark Deutel, Philipp Woller, Christopher Mutschler, Jürgen Teich
Large Deep Neural Networks (DNNs) are the backbone of today's artificial intelligence due to their ability to make accurate predictions when being trained on huge datasets.
no code implementations • 23 Mar 2022 • Jan Sommer, M. Akif Özkan, Oliver Keszocze, Jürgen Teich
The motivation of using SNNs over conventional neural networks is rooted in the special computational aspects of SNNs, especially the very high degree of sparsity of neural output activations.
no code implementations • 3 Mar 2022 • Christian Herglotz, Rafael Rosales, Michael Glass, Jürgen Teich, André Kaup
Finding the best possible encoding decisions for compressing a video sequence is a highly complex problem.
no code implementations • 29 Sep 2021 • Muhammad Sabih, Frank Hannig, Jürgen Teich
Our proposed architecture for dynamic pruning can be deployed on different hardware platforms.
no code implementations • 26 Aug 2020 • M. Akif Özkan, Burak Ok, Bo Qiao, Jürgen Teich, Frank Hannig
OpenVX promises to solve this issue for computer vision applications with a royalty-free industry standard that is based on a graph-execution model.
no code implementations • 18 Oct 2019 • Faramarz Khosravi, Alexander Raß, Jürgen Teich
This paper introduces an empirical approach that enables an efficient comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions.
no code implementations • 26 Feb 2015 • Oliver Reiche, Konrad Häublein, Marc Reichenbach, Frank Hannig, Jürgen Teich, Dietmar Fey
Therefore, in previous work, we have shown that elevating the description of image algorithms to an even higher abstraction level, by using a Domain-Specific Language (DSL), can significantly cut down the complexity for designing such algorithms for FPGAs.
no code implementations • 20 Aug 2014 • Moritz Schmid, Oliver Reiche, Christian Schmitt, Frank Hannig, Jürgen Teich
Multiresolution Analysis (MRA) is a mathematical method that is based on working on a problem at different scales.