lava
norse
| lava | norse | |
|---|---|---|
| 3 | 6 | |
| 504 | 613 | |
| 2.6% | 1.6% | |
| 8.2 | 6.4 | |
| 7 days ago | 6 days ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 or later | GNU Lesser General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
lava
- GitHub - lava-nc/lava: A Software Framework for Neuromorphic Computing
- Lava v0.4.0 just released, an open source software framework, community contributions encouraged
-
Programmability of neuromorphic chips these days?
Although it looks like Intel is trying hard to bring in the community with their Lava Framework which th why say let’s you develop ‘neuro-inspired’ apps that map to a neurotrophic computer. Might be worth checking out tbh?
norse
-
Neuromorphic learning, working memory, and metaplasticity in nanowire networks
This gives you a ludicrous advantage over current neural net accelerators. Specifically 3-5 orders is magnitude in energy and time, as demonstrated in the BranScaleS system https://www.humanbrainproject.eu/en/science-development/focu...
Unfortunately, that doesn't solve the problem of learning. Just because you can build efficient neuromorphic systems doesn't mean that we know how to train them. Briefly put, the problem is that a physical system has physical constraints. You can't just read the global state in NWN and use gradient descent as we would in deep learning. Rather, we have to somehow use local signals to approximate local behaviour that's helpful on a global scale. That's why they use Hebbian learning in the paper (what fires together, wires together), but it's tricky to get right and I haven't personally seen examples that scale to systems/problems of "interesting" sizes. This is basically the frontier of the field: we need local, but generalizable, learning rules that are stable across time and compose freely into higher-order systems.
Regarding educational material, I'm afraid I haven't seen great entries for learning about SNNs in full generality. I co-author a simulator (https://github.com/norse/norse/) based on PyTorch with a few notebook tutorials (https://github.com/norse/notebooks) that may be helpful.
I'm actually working on some open resources/course material for neuromorphic computing. So if you have any wishes/ideas, please do reach out. Like, what would a newcomer be looking for specifically?
-
[D] The Complete Guide to Spiking Neural Networks
Surrogate gradients and BPTT, this is what is implemented in Norse https://github.com/Norse/Norse. It is also possible to compute exact gradients using the Eventprop algorithm.
- [P] Norse - Deep learning with spiking neural networks (SNNs) in PyTorch
- Show HN: Deep learning with spiking neural networks (SNNs) in PyTorch
-
Don't Mess with Backprop: Doubts about Biologically Plausible Deep Learning
That repo is slightly outdated, development now continues at https://github.com/norse/norse.
What are some alternatives?
magicavoxel-shaders - A collection of shaders for MagicaVoxel to generate geometry, noise, patterns, and simplify common and repetitive tasks.
snntorch - Deep and online learning with spiking neural networks in Python
azure-sdk-for-python - This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://docs.microsoft.com/python/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-python.
Spiking-Neural-Network - Pure python implementation of SNN
spikingjelly - SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
python-performance - Repository for the book Fast Python - published by Manning
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Pentest-Service-Enumeration - Suggests programs to run against services found during the enumeration phase of a Pentest
bindsnet - Simulation of spiking neural networks (SNNs) using PyTorch.
pyshader - Write modern GPU shaders in Python!
ocaml-torch - OCaml bindings for PyTorch