Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Martin Hardieck"'
Autor:
Diego Botache, Florian Bethke, Martin Hardieck, Maarten Bieshaar, Ludwig Brabetz, Mohamed Ayeb, Peter Zipf, Bernhard Sick
Publikováno v:
2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6193f99a7316bd4a36af733232b5be69
https://doi.org/10.1109/acsos52086.2021.00031
https://doi.org/10.1109/acsos52086.2021.00031
Publikováno v:
IEEE Transactions on Computers. 66:2072-2080
Constant matrix multiplication (CMM), i.e., the multiplication of a constant matrix with a vector, is a common operation in digital signal processing. It is a generalization of multiple constant multiplication (MCM) where a single variable is multipl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b69f6e2bcfcbf0df46e48c001a029e8b
https://doi.org/10.1109/tc.2017.2701365
https://doi.org/10.1109/tc.2017.2701365
Autor:
Martin Kumm, Peter Zipf, Philip H. W. Leong, David Boland, Martin Hardieck, Stephen Tridgell, Duncan J. M. Moss
The computational complexity of neural networks for large scale or real-time applications necessitates hardware acceleration. Most approaches assume that the network architecture and parameters are unknown at design time, permitting usage in a large
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27eccd0cc9c3194270124dd1ebb7000e
http://arxiv.org/abs/1909.04509
http://arxiv.org/abs/1909.04509
Publikováno v:
FPGA
Convolutional neural networks (CNNs) gained great success in machine learning applications and much attention was paid to their acceleration on field programmable gate arrays (FPGAs). The most demanding computational complexity of CNNs is found in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ef3dc9bfa79c40cd3210e5eb7e7e414
https://doi.org/10.1145/3289602.3293905
https://doi.org/10.1145/3289602.3293905
Autor:
David Boland, Martin Hardieck, Xueyuan Liu, Martin Kumm, Philip H. W. Leong, Peter Zipf, Julian Faraone
Low-precision arithmetic operations to accelerate deep-learning applications on field-programmable gate arrays (FPGAs) have been studied extensively, because they offer the potential to save silicon area or increase throughput. However, these benefit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4002c70a5ad9d8fb373a0d6e8c58dd11
Publikováno v:
ICECS
Constant matrix multiplication (CMM), i.e., the multiplication of a constant matrix with a vector, is a common operation in digital signal processing. The CMM operation can be realized multiplierless using only additions/subtractions and bit shifts.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6c649d71de1af46fd06ac977aa208b7
https://doi.org/10.1109/icecs.2018.8617860
https://doi.org/10.1109/icecs.2018.8617860
Publikováno v:
FPL
The scaling operation, i. e., the multiplication with a single constant is a frequently used operation in many kinds of numeric algorithms. The multiple constant multiplication (MCM) is a generalization where a variable is multiplied by several const
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4b39461464240d6f6f196487e9c54e6
https://doi.org/10.1109/fpl.2013.6645543
https://doi.org/10.1109/fpl.2013.6645543