activity
20172023
most citedDemonstrating Advantages of Neuromorphic Computation: A Pilot Study

138 citations · 417 across the 12 of their papers we have counts for

collaborators

12 papers

cs.ET2023

From Clean Room to Machine Room: Commissioning of the First-Generation BrainScaleS Wafer-Scale Neuromorphic System

Hartmut Schmidt, José Montes, Andreas Grübl +9

The first-generation of BrainScaleS, also referred to as BrainScaleS-1, is a neuromorphic system for emulating large-scale networks of spiking neurons. Following a "physical modeli…

cs.NE2022

hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2

Philipp Spilger, Elias Arnold, Luca Blessing +4

Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a mac…

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.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★ 13 cited

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…

cs.NE2020★ 20 cited

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…

cs.NE2020★ 7 cited

The Operating System of the Neuromorphic BrainScaleS-1 System

Eric Müller, Sebastian Schmitt, Christian Mauch +19

BrainScaleS-1 is a wafer-scale mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing. The…

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★ 138 cited

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Timo Wunderlich, Akos F. Kungl, Eric Müller +14

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 com…

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…