Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Christian Klarhorst"'
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
We propose and discuss a platform overarching benchmark suite for neuromorphic hardware. This suite covers benchmarks from low-level characterization to high-level application evaluation using benchmark specific metrics. With this rather broad approa
Externí odkaz:
https://doaj.org/article/809d07b3edea4aca8956df2f818458be
Autor:
Denis Gollner, Peter WiBbrock, Marc Hesse, Magnus Redeker, Dennis Quirin, Simon Althoff, Christian Klarhorst
Today's high complexity and required expertise in various disciplines for data-based evaluations of shop-floor assets is challenging. This paper describes the ongoing development towards an Industry 4.0 ecosystem enabling Smart Services and shop-floo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03604be0dd7cf51f5f47a65e91c749f6
https://pub.uni-bielefeld.de/record/2957582
https://pub.uni-bielefeld.de/record/2957582
Publikováno v:
Artificial Neural Networks and Machine Learning – ICANN 2020 ISBN: 9783030616151
ICANN (2)
ICANN (2)
With more and more event-based neuromorphic hardware systems being developed at universities and in industry, there is a growing need for assessing their performance with domain specific measures. In this work, we use the methodology of converting pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09e0ee3a26233afa3f24ce716048e82a
https://doi.org/10.1007/978-3-030-61616-8_49
https://doi.org/10.1007/978-3-030-61616-8_49
Publikováno v:
HPCS
In the field of neuromorphic computing several hardware accelerators for spiking neural networks have been introduced, but few studies actually compare different systems. These comparative studies reveal difficulties in porting an existing network to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09e59339b3fe890bdafe0611e22c3b4a
https://doi.org/10.1109/hpcs48598.2019.9188207
https://doi.org/10.1109/hpcs48598.2019.9188207
Autor:
Wayne Kelly, Neela Gayen, Thorsten Jungeblut, Christian Klarhorst, Maolin Tang, Johannes Ax, Martin Flasskamp
Publikováno v:
PDP
Embedded streaming applications are facing increasingly demanding performance requirements in terms of throughput. A common mechanism for providing high compute power with a low energy budget is to use a very large number of low-power cores, often in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a3187502c552bbb7036026415c26b9a
https://doi.org/10.1109/pdp2018.2018.00062
https://doi.org/10.1109/pdp2018.2018.00062
Autor:
Mario Porrmann, Wayne Kelly, Christian Klarhorst, Martin Flasskamp, Thorsten Jungeblut, Michael Thies, Johannes Ax, Gregor Sievers
Publikováno v:
RAPIDO@HiPEAC
Parallel programming and effective partitioning of applications for embedded many-core architectures requires optimization algorithms. However, these algorithms have to quickly evaluate thousands of different partitions. We present a fast performance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::463f6adbdcda54b9ad44f751a231d119
https://doi.org/10.1145/2852339.2852342
https://doi.org/10.1145/2852339.2852342
Autor:
Christoph Ragg, Johannes Ax, Jianing Chen, Wayne Kelly, Gregor Sievers, Martin Flasskamp, Andrew Sorensen, Thorsten Jungeblut, Christian Klarhorst
Publikováno v:
ISSoC
Energy efficient embedded computing enables new application scenarios in mobile devices like software-defined radio and video processing. The hierarchical multiprocessor considered in this work may contain dozens or hundreds of resource efficient VLI
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9ed7439fabe974892ffd0cf6b62141d
https://doi.org/10.1109/issoc.2014.6972436
https://doi.org/10.1109/issoc.2014.6972436
Publikováno v:
Proceedings of the Neuro-inspired Computational Elements Workshop
NICE
NICE
With more and more neuromorphic hardware systems for the accel- eration of spiking neural networks available in science and industry, there is a demand for platform comparison and performance esti- mation of such systems. This work describes selected
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3008140b19dee7412a021b6ad2df61a8