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20202023
most citedSpiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication

17 citations · 59 across the 8 of their papers we have counts for

collaborators

9 papers

eess.SP2023★ 17 cited

Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication

Elias Arnold, Georg Böcherer, Florian Strasser +7

Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth…

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.NE2022★ 2 cited

The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity

Christian Pehle, Sebastian Billaudelle, Benjamin Cramer +7

Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives. Brain-inspired…

cs.AR2021★ 16 cited

Demonstrating Analog Inference on the BrainScaleS-2 Mobile System

Yannik Stradmann, Sebastian Billaudelle, Oliver Breitwieser +8

We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocar…

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

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.NE2020★ 2 cited

Accelerated Analog Neuromorphic Computing

Johannes Schemmel, Sebastian Billaudelle, Phillip Dauer +1

This paper presents the concepts behind the BrainScales (BSS) accelerated analog neuromorphic computing architecture. It describes the second-generation BrainScales-2 (BSS-2) versi…