Frontiers in Neuroscience (May 2022)
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
- 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,
- Andreas Baumbach,
- Sebastian Billaudelle,
- Benjamin Cramer,
- Falk Ebert,
- Julian Göltz,
- Julian Göltz,
- Joscha Ilmberger,
- Vitali Karasenko,
- Mitja Kleider,
- Aron Leibfried,
- Christian Pehle,
- Johannes Schemmel
Affiliations
- Eric Müller
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Elias Arnold
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Oliver Breitwieser
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Milena Czierlinski
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Arne Emmel
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Jakob Kaiser
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Christian Mauch
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Sebastian Schmitt
- Third Institute of Physics, University of Göttingen, Göttingen, Germany
- Philipp Spilger
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Raphael Stock
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Yannik Stradmann
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Johannes Weis
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Andreas Baumbach
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Andreas Baumbach
- Department of Physiology, University of Bern, Bern, Switzerland
- Sebastian Billaudelle
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Benjamin Cramer
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Falk Ebert
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Julian Göltz
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Julian Göltz
- Department of Physiology, University of Bern, Bern, Switzerland
- Joscha Ilmberger
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Vitali Karasenko
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Mitja Kleider
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Aron Leibfried
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Christian Pehle
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Johannes Schemmel
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- DOI
- https://doi.org/10.3389/fnins.2022.884128
- Journal volume & issue
-
Vol. 16
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.
Keywords
- hardware abstraction
- neuroscientific modeling
- accelerator
- analog computing
- neuromorphic
- embedded operation