SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
This provides a practical toolkit for researchers and engineers working on neuromorphic computing, though it is incremental as it builds on existing SNN methods.
The authors tackled the lack of efficient programming frameworks for spiking neural networks (SNNs) by developing SpikingJelly, an open-source infrastructure platform that accelerates SNN training by 11x and supports deployment on neuromorphic chips.
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.