Home > Publications database > SNNAX-Spiking Neural Networks in JAX > print

001     1038043
005     20250203103306.0
024 7 _ |a 10.34734/FZJ-2025-01092
|2 datacite_doi
037 _ _ |a FZJ-2025-01092
100 1 _ |a Lohoff, Jamie
|0 P:(DE-Juel1)192147
|b 0
|u fzj
111 2 _ |a 2024 International Conference on Neuromorphic Systems (ICONS)
|c Arlington, Virginia
|d 2024-07-30 - 2024-08-02
|w USA
245 _ _ |a SNNAX-Spiking Neural Networks in JAX
260 _ _ |c 2024
300 _ _ |a 251 - 255
336 7 _ |a CONFERENCE_PAPER
|2 ORCID
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a Output Types/Conference Paper
|2 DataCite
336 7 _ |a Contribution to a conference proceedings
|b contrib
|m contrib
|0 PUB:(DE-HGF)8
|s 1738246115_31341
|2 PUB:(DE-HGF)
520 _ _ |a Spiking Neural Networks (SNNs) simulators are essential tools to prototype biologically inspired models and neuromorphic hardware architectures and predict their performance. For such a tool, ease of use and flexibility are critical, but so is simulation speed especially given the complexity inherent to simulating SNN. Here, we present SNNAX, a JAX-based framework for simulating and training such models with PyTorch-like intuitiveness and JAX-like execution speed. SNNAX models are easily extended and customized to fit the desired model specifications and target neuromorphic hardware. Additionally, SNNAX offers key features for optimizing the training and deployment of SNNs such as flexible automatic differentiation and just-in-time compilation. We evaluate and compare SNNAX to other commonly used machine learning (ML) frameworks used for programming SNNs. We provide key performance metrics, best practices, documented examples for simulating SNNs in SNNAX, and implement several benchmarks used in the literature.
536 _ _ |a 5234 - Emerging NC Architectures (POF4-523)
|0 G:(DE-HGF)POF4-5234
|c POF4-523
|f POF IV
|x 0
536 _ _ |a GREENEDGE - Taming the environmental impact of mobile networks through GREEN EDGE computing platforms (953775)
|0 G:(EU-Grant)953775
|c 953775
|f H2020-MSCA-ITN-2020
|x 1
536 _ _ |a BMBF 03ZU1106CA - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - A (03ZU1106CA)
|0 G:(BMBF)03ZU1106CA
|c 03ZU1106CA
|x 2
536 _ _ |a BMBF 03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - B (BMBF-03ZU1106CB)
|0 G:(DE-Juel1)BMBF-03ZU1106CB
|c BMBF-03ZU1106CB
|x 3
536 _ _ |a BMBF 16ME0400 - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (16ME0400)
|0 G:(BMBF)16ME0400
|c 16ME0400
|x 4
700 1 _ |a Finkbeiner, Jan
|0 P:(DE-Juel1)190112
|b 1
|u fzj
700 1 _ |a Neftci, Emre
|0 P:(DE-Juel1)188273
|b 2
|u fzj
856 4 _ |u https://ieeexplore.ieee.org/document/10766537
856 4 _ |u https://juser.fz-juelich.de/record/1038043/files/SNNAX-Spiking%20Neural%20Networks%20in%20JAX.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1038043
|p openaire
|p open_access
|p driver
|p VDB
|p ec_fundedresources
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)192147
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)190112
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)188273
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-523
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Neuromorphic Computing and Network Dynamics
|9 G:(DE-HGF)POF4-5234
|x 0
914 1 _ |y 2024
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)PGI-15-20210701
|k PGI-15
|l Neuromorphic Software Eco System
|x 0
980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)PGI-15-20210701
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21