A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.
[ ] Tests for the changes have been added (for bug fixes/features)
[ ] Docs have been added / updated (for bug fixes / features)
[ ] I have performed a self-review of my code
[ ] Will this be part of a product update? If yes, please write one line about this on the CHANGELOG.md
What kind of change does this PR introduce? (Bug fix, feature, docs update, ...)
New feature, including extensive changes in code, documentation and unit tests.
What is the current behavior? (You can also link to an open issue here)
So far, only sequential models can be deployed on DynapCNN chips.
What is the new behavior (if this is a feature change)?
Allow non-sequential SNNs (e.g. residuals, recurrent, ...) to be deployed on DynapCNN chips.
Does this PR introduce a breaking change? (What changes might users need to make in their application due to this PR?)
Potentially. Ideally for sequential SNNs there are no (or minimal) breaking changes, but this has to be assessed when reviewing this PR.
Other information:
The changes are quite extensive, so reviewing this will take some time.
Checklist before requesting a review
[ ] Tests for the changes have been added (for bug fixes/features)
[ ] Docs have been added / updated (for bug fixes / features)
[ ] I have performed a self-review of my code
[ ] Will this be part of a product update? If yes, please write one line about this on the CHANGELOG.md
What kind of change does this PR introduce? (Bug fix, feature, docs update, ...) New feature, including extensive changes in code, documentation and unit tests.
What is the current behavior? (You can also link to an open issue here) So far, only sequential models can be deployed on DynapCNN chips.
What is the new behavior (if this is a feature change)? Allow non-sequential SNNs (e.g. residuals, recurrent, ...) to be deployed on DynapCNN chips.
Does this PR introduce a breaking change? (What changes might users need to make in their application due to this PR?) Potentially. Ideally for sequential SNNs there are no (or minimal) breaking changes, but this has to be assessed when reviewing this PR.
Other information: The changes are quite extensive, so reviewing this will take some time.