Müller, Eric; Arnold, Elias; Breitwieser, Oliver; Czierlinski, Milena; Emmel, Arne; Kaiser, Jakob; Mauch, Christian; Schmitt, Sebastian; Spilger, Philipp; Stock, Raphael; Stradmann, Yannik; Weis, Johannes; Baumbach, Andreas; Billaudelle, Sebastian; Cramer, Benjamin; Ebert, Falk; Göltz, Julian; Ilmberger, Joscha; Karasenko, Vitali; Kleider, Mitja; ... (2022). A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware. Frontiers in neuroscience, 16, p. 884128. Frontiers Research Foundation 10.3389/fnins.2022.884128
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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.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Physiology |
UniBE Contributor: |
Göltz, Julian |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1662-4548 |
Publisher: |
Frontiers Research Foundation |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
08 Jun 2022 08:03 |
Last Modified: |
05 Dec 2022 16:20 |
Publisher DOI: |
10.3389/fnins.2022.884128 |
PubMed ID: |
35663548 |
Uncontrolled Keywords: |
accelerator analog computing embedded operation hardware abstraction local learning neuromorphic neuroscientific modeling |
BORIS DOI: |
10.48350/170467 |
URI: |
https://boris.unibe.ch/id/eprint/170467 |
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