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Computer Science > Neural and Evolutionary Computing

arXiv:2203.11102 (cs)
[Submitted on 21 Mar 2022]

Title:A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware

Authors: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, Johannes Schemmel
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Abstract:Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability and efficiency.
Subjects: Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2203.11102 [cs.NE]
  (or arXiv:2203.11102v1 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.2203.11102

Submission history

From: Eric Müller [view email]
[v1] Mon, 21 Mar 2022 16:30:18 UTC (1,615 KB)
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