activity
20172022
most citedNeuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

126 citations · 241 across the 8 of their papers we have counts for

collaborators

10 papers

q-bio.NC2022★ 1 cited

Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware

Benjamin Cramer, Markus Kreft, Sebastian Billaudelle +9

A unique feature of neuromorphic computing is that memory is an implicit part of processing through traces of past information in the system's collective dynamics. The extent of me…

cs.NE2022

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…

cs.AR2022★ 1 cited

Demonstrating BrainScaleS-2 Inter-Chip Pulse-Communication using EXTOLL

Tobias Thommes, Sven Bordukat, Andreas Grübl +3

The BrainScaleS-2 (BSS-2) Neuromorphic Computing System currently consists of multiple single-chip setups, which are connected to a compute cluster via Gigabit-Ethernet network tec…

cs.NE2020★ 8 cited

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…

cs.NE2020

Surrogate gradients for analog neuromorphic computing

Benjamin Cramer, Sebastian Billaudelle, Simeon Kanya +9

To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum but communicate with spikes, binary events in time. Analog neu…

cs.AR2020

Verification and Design Methods for the BrainScaleS Neuromorphic Hardware System

Andreas Grübl, Sebastian Billaudelle, Benjamin Cramer +2

This paper presents verification and implementation methods that have been developed for the design of the BrainScaleS-2 65nm ASICs. The 2nd generation BrainScaleS chips are mixed-…

q-bio.NC2019★ 43 cited

Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate

Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber +22

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 comp…

cs.NE2018★ 41 cited

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…

cs.NE2017★ 126 cited

Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

Sebastian Schmitt, Johann Klaehn, Guillaume Bellec +22

Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energ…

q-bio.NC2017★ 21 cited

Pattern representation and recognition with accelerated analog neuromorphic systems

Mihai A. Petrovici, Sebastian Schmitt, Johann Klähn +33

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 partic…