| Jahr | 2022 |
| Autor(en) | Müller E, Arnold E, Breitwieser O, Czierlinski M, Emmel A, Kaiser J, Mauch C, Schmitt S, Spilger P, Stock R, Stradmann Y, Weis J, Baumbach A, Billaudelle S, Cramer B, Ebert F, Göltz J, Ilmberger J, Karasenko V, Kleider M, Leibfried A, Pehle C and Schemmel J |
| Titel | A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware |
| KIP-Nummer | HD-KIP 22-30 |
| KIP-Gruppe(n) | F9 |
| Dokumentart | Paper |
| Keywords (angezeigt) | hardware abstraction, neuroscientific modeling, accelerator, analog computing, neuromorphic, embedded operation, local learning |
| Quelle | Front. Neurosci. 16 (2022) 884128 |
| doi | 10.3389/fnins.2022.884128 |
| Abstract (en) | 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. |
| bibtex | @article{mueller2022scalable,
author = {Müller E, Arnold E, Breitwieser O, Czierlinski M, Emmel A, Kaiser J, Mauch C, Schmitt S, Spilger P, Stock R, Stradmann Y, Weis J, Baumbach A, Billaudelle S, Cramer B, Ebert F, Göltz J, Ilmberger J, Karasenko V, Kleider M, Leibfried A, Pehle C and Schemmel J},
title = {A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware},
journal = {Front. Neurosci.},
year = {2022},
volume = {16},
number = {884128},
pages = {},
month = {may},
doi = {10.3389/fnins.2022.884128}
} |
| Datei |
