# fangwei123456/spikingjelly

SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.

Repository: https://github.com/fangwei123456/spikingjelly
Canonical: https://ross.abutalabs.com/products/spikingjelly
Homepage: https://spikingjelly.readthedocs.io
Language: Python
License: NOASSERTION
License Family: other
Topics: pytorch, spiking-neural-networks, snn, deep-learning, machine-learning, dvs
Last push: 2026-08-26T18:09:36+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 2451, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2110, forks 319 (observed 2026-08-28T04:06:14.234236+00:00)

## What it is
SpikingJelly is an open-source deep learning framework for Spiking Neural Networks (SNNs) built on PyTorch. It provides a beginner-friendly API for defining, training, and deploying SNNs, including ANN-to-SNN conversion, event-based datasets, and acceleration backends like torch, cupy, and triton.

## Use cases
- build and train spiking neural networks with pytorch
- convert trained ANNs to energy-efficient SNNs
- train SNNs on event-based vision datasets like DVS
- simulate LIF neurons with surrogate gradient learning
- deploy spiking neural networks to neuromorphic hardware
- accelerate SNN training with cupy or triton backends
- run large-scale distributed SNN training

## When to choose
- you want to experiment with spiking neural networks in a PyTorch-native way
- you need ANN-to-SNN conversion for energy-efficient inference
- you work with event-based camera datasets (DVS, N-MNIST, CIFAR10-DVS)
- you need a mature, documented SNN framework with active development

## When to avoid
- you need conventional deep learning without spiking neurons
- you require a framework outside the PyTorch ecosystem
- you need production deployment on non-neuromorphic hardware without SNN support

## Facets
- artifact type: framework
- maturity: active
- function: deep-learning, machine-learning, simulation
- domain: deep-learning, machine-learning, computer-vision
- platform: python, cross-platform
- tags: spiking-neural-networks, snn, pytorch, ann2snn, neuromorphic, event-based-vision, dvs, surrogate-gradients, neuromorphic-computing, gpu

## Member repositories
- fangwei123456/spikingjelly (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:14.234236+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:53:44.789449+00:00, confidence not recorded.
  - readme: https://github.com/fangwei123456/spikingjelly (fetched 2026-08-28T04:06:14.234236+00:00, sha 2f4a6d0e2490)
  - registry_pypi: https://pypi.org/pypi/spikingjelly/json (fetched 2026-08-29T10:34:25.101733+00:00, sha 45cbc980af42)
- Data as of 2026-08-30T08:39:29.467469+00:00.