SpikingJelly: An open-source machine learning infrastructure platform
  for spike-based intelligence
Wei Fang Yanqi Chen Jianhao Ding Zhaofei Yu Timothée Masquelier Ding Chen Liwei Huang Huihui Zhou Guoqi Li Yonghong Tian
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 pre-processing 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 $11\times$, 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.