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001038043 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-01092
001038043 037__ $$aFZJ-2025-01092
001038043 1001_ $$0P:(DE-Juel1)192147$$aLohoff, Jamie$$b0$$ufzj
001038043 1112_ $$a2024 International Conference on Neuromorphic Systems (ICONS)$$cArlington, Virginia$$d2024-07-30 - 2024-08-02$$wUSA
001038043 245__ $$aSNNAX-Spiking Neural Networks in JAX
001038043 260__ $$c2024
001038043 300__ $$a251 - 255
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001038043 520__ $$aSpiking Neural Networks (SNNs) simulators are essential tools to prototype biologically inspired models and neuromorphic hardware architectures and predict their performance. For such a tool, ease of use and flexibility are critical, but so is simulation speed especially given the complexity inherent to simulating SNN. Here, we present SNNAX, a JAX-based framework for simulating and training such models with PyTorch-like intuitiveness and JAX-like execution speed. SNNAX models are easily extended and customized to fit the desired model specifications and target neuromorphic hardware. Additionally, SNNAX offers key features for optimizing the training and deployment of SNNs such as flexible automatic differentiation and just-in-time compilation. We evaluate and compare SNNAX to other commonly used machine learning (ML) frameworks used for programming SNNs. We provide key performance metrics, best practices, documented examples for simulating SNNs in SNNAX, and implement several benchmarks used in the literature.
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001038043 536__ $$0G:(BMBF)16ME0400$$aBMBF 16ME0400 - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (16ME0400)$$c16ME0400$$x4
001038043 7001_ $$0P:(DE-Juel1)190112$$aFinkbeiner, Jan$$b1$$ufzj
001038043 7001_ $$0P:(DE-Juel1)188273$$aNeftci, Emre$$b2$$ufzj
001038043 8564_ $$uhttps://ieeexplore.ieee.org/document/10766537
001038043 8564_ $$uhttps://juser.fz-juelich.de/record/1038043/files/SNNAX-Spiking%20Neural%20Networks%20in%20JAX.pdf$$yOpenAccess
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001038043 9141_ $$y2024
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