Demonstrating BrainScaleS-2 Inter-Chip Pulse-Communication using EXTOLL-Reference-Cited by-同舟云学术

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

Published:2022-03-28 Issue: Volume: Page:
ISSN:
Container-title:Neuro-Inspired Computational Elements Conference
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Author:

Thommes Tobias¹,Bordukat Sven¹,Grübl Andreas¹,Karasenko Vitali¹,Müller Eric¹,Schemmel Johannes¹

Affiliation:

1. Kirchhoff-Institute for Physics, University of Heidelberg, Germany

Funder

Deutsche Forschungsgemeinschaft

European Commission

Helmholtz Association Initiative and Networking Fund (Advanced Computing Architectures, ACA)

Bundesministerium für Bildung und Forschung

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3517343.3517376

Reference15 articles.

1. Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate

2. On Achieving High Message Rates

3. Verification and Design Methods for the BrainScaleS Neuromorphic Hardware System

4. VELO: A Novel Communication Engine for Ultra-Low Latency Message Transfers

5. Eric Müller Elias Arnold Oliver Breitwieser Milena Czierlinski Arne Emmel Jakob Kaiser Christian Mauch Sebastian Schmitt Philipp Spilger Raphael Stock Yannik Stradmann Johannes Weis Andreas Baumbach Sebastian Billaudelle Benjamin Cramer Falk Ebert Julian Göltz Joscha Ilmberger Vitali Karasenko Mitja Kleider Aron Leibfried Christian Pehle and Johannes Schemmel. 2022. A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware. Frontiers in Neuromorphic Engineering(2022). Submitted. Eric Müller Elias Arnold Oliver Breitwieser Milena Czierlinski Arne Emmel Jakob Kaiser Christian Mauch Sebastian Schmitt Philipp Spilger Raphael Stock Yannik Stradmann Johannes Weis Andreas Baumbach Sebastian Billaudelle Benjamin Cramer Falk Ebert Julian Göltz Joscha Ilmberger Vitali Karasenko Mitja Kleider Aron Leibfried Christian Pehle and Johannes Schemmel. 2022. A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware. Frontiers in Neuromorphic Engineering(2022). Submitted.

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1. Variational learning of quantum ground states on spiking neuromorphic hardware;iScience;2022-08

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