.. This file is generated from CHANGELOG.md.
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变更日志 | Changelog
====================

All notable changes to SpikingJelly are documented in this file.

SpikingJelly starts maintaining this standard changelog from ``2.0.0.dev0``.
For older releases, see the historical fatal-bug record in
`bugs.md <https://github.com/fangwei123456/spikingjelly/blob/master/bugs.md>`__
and the archived documentation linked from the project README.

Unreleased
----------

Features
~~~~~~~~

- None.

Improvements
~~~~~~~~~~~~

- None.

Bug Fixes
~~~~~~~~~

- None.

Breaking Changes and Notices
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- None.

2.0.0.dev0 - 2026-07-09
-----------------------

This entry summarizes the user-visible changes since the previous PyPI stable release, ``0.0.0.0.14`` (``2941330``), through ``2.0.0.dev0`` (``b4f3b68``).

Features
~~~~~~~~

ANN-to-SNN Conversion
^^^^^^^^^^^^^^^^^^^^^

Module: ``spikingjelly.activation_based.ann2snn``.

- Added a redesigned conversion subsystem with recipe-based workflows.

- Added conversion recipes and examples for LTB,
  STA-style Transformer conversion, and SpikeZIP QANN/Transformer experiments.

Few-Spike and Activation-Aware Neurons
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Modules: ``spikingjelly.activation_based.neuron``

- Added few-spike neuron for ann2snn research.
- Added activation-aware IF neuron for ann2snn research.

Memory Optimization
^^^^^^^^^^^^^^^^^^^

Module: ``spikingjelly.activation_based.memopt``.

- Added the training memory optimization pipeline with gradient checkpointing and spike compression.

Precision
^^^^^^^^^

Module: ``spikingjelly.activation_based.precision``.

- Added a common precision policy interface for configuring precision behavior
  without depending on backend-specific implementation details.

Distributed Training
^^^^^^^^^^^^^^^^^^^^

Module: ``spikingjelly.activation_based.distributed``.

- Added distributed training and DTensor utilities for larger-scale SNN
  experiments.

Profiling and Energy Estimation
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Module: ``spikingjelly.activation_based.op_counter``.

- Added operation counting tools for profiling SNN models.
- Added inference energy estimation tools.

Improvements
~~~~~~~~~~~~

- Updated the package version scheme from legacy ``0.0.0.0.X`` numbering to
  PEP 440 compatible V2 versions.

- Raised the runtime baseline to Python ``>=3.11`` and ``torch>=2.6.0``.

- Updated README and documentation pages for the V2 release policy,
  pre-release installation, and pre-V2 dependency pinning.

- Refactored ``spikingjelly.visualizing`` into focused submodules.

- Refactored the official website.
- Added broader tutorials and API documentation.
- Reworked public API documentation and docstrings across the project.

- Refined datasets, timing-based modules, exchange utilities, backend kernels,
  model helpers, and training utilities across the V2 development line.

- Added broader regression tests for V2.

Bug Fixes
~~~~~~~~~

- Fixed neuron initialization edge cases.

- Fixed reset-state handling edge cases.

- Fixed spiking RNN hidden-state dtype handling.

- Fixed CuPy and Triton backend dispatch issues for neuron evaluation paths.

- Fixed dataset preprocessing edge cases.

- Fixed publication metadata cleanup edge cases.

- Hardened ANN-to-SNN conversion validation and calibration.

- Hardened ANN-to-SNN step-mode, mask-handling, download, and module-replacement
  paths.

- Fixed documentation rendering, tutorial, and API navigation issues.

Breaking Changes and Notices
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- V2 starts a new compatibility generation. Projects that must remain on the
  legacy release line should pin ``spikingjelly<2``.

- Some experimental or internal ANN2SNN conversion interfaces were refactored
  around the V2 recipe and operator model.

- Documentation structure and public API pages were reorganized; external links
  to old generated API pages may need to be updated.

- Before upgrading from ``0.0.0.0.14``, review this changelog and the V2 README
  installation notes.

- Conservative projects should pin ``spikingjelly<2`` until they are ready to
  validate V2 behavior.

- To test published V2 pre-releases, install with
  ``pip install --pre spikingjelly``.

- For source installs, follow the current README and ensure the selected
  PyTorch build matches the target CPU/CUDA environment.