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Sebastian Billaudelle
Sebastian Billaudelle
Unknown affiliation
Verified email at kip.uni-heidelberg.de
Title
Cited by
Cited by
Year
Demonstrating advantages of neuromorphic computation: a pilot study
T Wunderlich, AF Kungl, E Müller, A Hartel, Y Stradmann, SA Aamir, ...
Frontiers in neuroscience 13, 260, 2019
852019
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
S Billaudelle, Y Stradmann, K Schreiber, B Cramer, A Baumbach, D Dold, ...
2020 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2020
422020
Porting HTM models to the Heidelberg neuromorphic computing platform
S Billaudelle, S Ahmad
arXiv preprint arXiv:1505.02142, 2015
422015
Fast and deep neuromorphic learning with first-spike coding
J Göltz, A Baumbach, S Billaudelle, AF Kungl, O Breitwieser, K Meier, ...
Proceedings of the neuro-inspired computational elements workshop, 1-3, 2020
37*2020
Fast and energy-efficient neuromorphic deep learning with first-spike times
J Göltz, L Kriener, A Baumbach, S Billaudelle, O Breitwieser, B Cramer, ...
Nature machine intelligence 3 (9), 823-835, 2021
272021
Verification and design methods for the brainscales neuromorphic hardware system
A Grübl, S Billaudelle, B Cramer, V Karasenko, J Schemmel
Journal of Signal Processing Systems 92 (11), 1277-1292, 2020
202020
Structural plasticity on an accelerated analog neuromorphic hardware system
S Billaudelle, B Cramer, MA Petrovici, K Schreiber, D Kappel, ...
Neural networks 133, 11-20, 2021
192021
Accelerated analog neuromorphic computing
J Schemmel, S Billaudelle, P Dauer, J Weis
Analog Circuits for Machine Learning, Current/Voltage/Temperature Sensors …, 2022
182022
Training spiking multi-layer networks with surrogate gradients on an analog neuromorphic substrate
B Cramer, S Billaudelle, S Kanya, A Leibfried, A Grübl, V Karasenko, ...
arXiv preprint arXiv:2006.07239, 2020
162020
Surrogate gradients for analog neuromorphic computing
B Cramer, S Billaudelle, S Kanya, A Leibfried, A Grübl, V Karasenko, ...
Proceedings of the National Academy of Sciences 119 (4), e2109194119, 2022
142022
hxtorch: PyTorch for BrainScaleS-2
P Spilger, E Müller, A Emmel, A Leibfried, C Mauch, C Pehle, J Weis, ...
IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile …, 2020
142020
The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity
C Pehle, S Billaudelle, B Cramer, J Kaiser, K Schreiber, Y Stradmann, ...
Frontiers in Neuroscience 16, 2022
122022
The operating system of the neuromorphic brainScaleS-1 system
E Müller, S Schmitt, C Mauch, S Billaudelle, A Grübl, M Güttler, ...
arXiv preprint arXiv:2003.13749, 2020
112020
Inference with artificial neural networks on analog neuromorphic hardware
J Weis, P Spilger, S Billaudelle, Y Stradmann, A Emmel, E Müller, ...
IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile …, 2020
102020
Spiking neuromorphic chip learns entangled quantum states
S Czischek, A Baumbach, S Billaudelle, B Cramer, L Kades, ...
SciPost Physics 12 (1), 039, 2022
92022
Emulating dendritic computing paradigms on analog neuromorphic hardware
J Kaiser, S Billaudelle, E Müller, C Tetzlaff, J Schemmel, S Schmitt
Neuroscience 489, 290-300, 2022
72022
Design and implementation of a short term plasticity circuit for a 65 nm neuromorphic hardware system
S Billaudelle
Masterarbeit, Universität Heidelberg, 2017
72017
Accelerated analog neuromorphic computing. arXiv [Preprint](2020)
J Schemmel, S Billaudelle, P Dauer, J Weis
arXiv preprint arXiv:2003.11996 12, 2003
62003
Characterisation and Calibration of Short Term Plasticity on a Neuromorphic Hardware Chip
S Billaudelle
52014
A scalable approach to modeling on accelerated neuromorphic hardware
E Müller, E Arnold, O Breitwieser, M Czierlinski, A Emmel, J Kaiser, ...
Frontiers in neuroscience 16, 2022
42022
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