94 citations · 390 across the 16 of their papers we have counts for
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A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Eric Müller, Elias Arnold, Oliver Breitwieser +20
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This wor…
Inference with Artificial Neural Networks on Analog Neuromorphic Hardware
Johannes Weis, Philipp Spilger, Sebastian Billaudelle +11
The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neur…
hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware
Philipp Spilger, Eric Müller, Arne Emmel +10
We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks. The accelerator hard…
Extending BrainScaleS OS for BrainScaleS-2
Eric Müller, Christian Mauch, Philipp Spilger +5
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 f…
Fast and energy-efficient neuromorphic deep learning with first-spike times
Julian Göltz, Laura Kriener, Andreas Baumbach +9
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems are optimized for sho…
Accelerated physical emulation of Bayesian inference in spiking neural networks
Akos F. Kungl, Sebastian Schmitt, Johann Klähn +21
The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the…