Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Stoeckel, David A."'
Publikováno v:
IEEE International Intelligent Transportation Systems Conference (ITSC), 2021
Automated vehicles need to detect and classify objects and traffic participants accurately. Reliable object classification using automotive radar sensors has proved to be challenging. We propose a method that combines classical radar signal processin
Externí odkaz:
http://arxiv.org/abs/2202.08519
Autor:
Müller, Eric, Mauch, Christian, Spilger, Philipp, Breitwieser, Oliver Julien, Klähn, Johann, Stöckel, David, Wunderlich, Timo, Schemmel, Johannes
BrainScaleS-2 is a mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing. To augment its flexibility, the analog neural network core is accompanied by an embedd
Externí odkaz:
http://arxiv.org/abs/2003.13750
Autor:
Billaudelle, Sebastian, Stradmann, Yannik, Schreiber, Korbinian, Cramer, Benjamin, Baumbach, Andreas, Dold, Dominik, Göltz, Julian, Kungl, Akos F., Wunderlich, Timo C., Hartel, Andreas, Müller, Eric, Breitwieser, Oliver, Mauch, Christian, Kleider, Mitja, Grübl, Andreas, Stöckel, David, Pehle, Christian, Heimbrecht, Arthur, Spilger, Philipp, Kiene, Gerd, Karasenko, Vitali, Senn, Walter, Petrovici, Mihai A., Schemmel, Johannes, Meier, Karlheinz
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control. The high acceleration factor of
Externí odkaz:
http://arxiv.org/abs/1912.12980
Autor:
Cramer, Benjamin, Stöckel, David, Kreft, Markus, Wibral, Michael, Schemmel, Johannes, Meier, Karlheinz, Priesemann, Viola
The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurre
Externí odkaz:
http://arxiv.org/abs/1909.08418
Autor:
Wunderlich, Timo, Kungl, Akos F., Müller, Eric, Hartel, Andreas, Stradmann, Yannik, Aamir, Syed Ahmed, Grübl, Andreas, Heimbrecht, Arthur, Schreiber, Korbinian, Stöckel, David, Pehle, Christian, Billaudelle, Sebastian, Kiene, Gerd, Mauch, Christian, Schemmel, Johannes, Meier, Karlheinz, Petrovici, Mihai A.
Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a s
Externí odkaz:
http://arxiv.org/abs/1811.03618
Autor:
Petrovici, Mihai A., Schmitt, Sebastian, Klähn, Johann, Stöckel, David, Schroeder, Anna, Bellec, Guillaume, Bill, Johannes, Breitwieser, Oliver, Bytschok, Ilja, Grübl, Andreas, Güttler, Maurice, Hartel, Andreas, Hartmann, Stephan, Husmann, Dan, Husmann, Kai, Jeltsch, Sebastian, Karasenko, Vitali, Kleider, Mitja, Koke, Christoph, Kononov, Alexander, Mauch, Christian, Müller, Eric, Müller, Paul, Partzsch, Johannes, Pfeil, Thomas, Schiefer, Stefan, Scholze, Stefan, Subramoney, Anand, Thanasoulis, Vasilis, Vogginger, Bernhard, Legenstein, Robert, Maass, Wolfgang, Schüffny, René, Mayr, Christian, Schemmel, Johannes, Meier, Karlheinz
Publikováno v:
Circuits and Systems (ISCAS), 2017 IEEE International Symposium on
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks. In this paper, we review several possibilites to reverse m
Externí odkaz:
http://arxiv.org/abs/1703.06043
Akademický článek
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Publikováno v:
In Chest October 2023 164(4) Supplement:A5226-A5226
Akademický článek
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Publikováno v:
In Chest October 2022 162(4) Supplement:A1191-A1191