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SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.
Fang, Wei; Chen, Yanqi; Ding, Jianhao; Yu, Zhaofei; Masquelier, Timothée; Chen, Ding; Huang, Liwei; Zhou, Huihui; Li, Guoqi; Tian, Yonghong.
Afiliación
  • Fang W; School of Computer Science, Peking University, China.
  • Chen Y; Peng Cheng Laboratory, China.
  • Ding J; School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, China.
  • Yu Z; School of Computer Science, Peking University, China.
  • Masquelier T; Peng Cheng Laboratory, China.
  • Chen D; School of Computer Science, Peking University, China.
  • Huang L; Institute for Artificial Intelligence, Peking University, China.
  • Zhou H; Centre de Recherche Cerveau et Cognition (CERCO), UMR5549 CNRS-Université Toulouse 3, France.
  • Li G; Peng Cheng Laboratory, China.
  • Tian Y; Department of Computer Science and Engineering, Shanghai Jiao Tong University, China.
Sci Adv ; 9(40): eadi1480, 2023 10 06.
Article en En | MEDLINE | ID: mdl-37801497
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Neuronas Idioma: En Revista: Sci Adv Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Neuronas Idioma: En Revista: Sci Adv Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos