spikingjelly
CUDA-Guide
| spikingjelly | CUDA-Guide | |
|---|---|---|
| 1 | 2 | |
| 1,159 | 47 | |
| - | - | |
| 8.7 | 4.0 | |
| 6 days ago | 4 months ago | |
| Python | Cuda | |
| GNU General Public License v3.0 or later | - |
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spikingjelly
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Has anyone used Spiking Neural Networks (SNNs) for image processing?
Surrogate gradient learning w/ backpropagation: for short, you can use backpropagation with SNNs (by a little trick during the backward pass). Super easy to implement, super efficient. You have a deep SNN trained via backprop with any type of input you want. Personally, that is completely my jam. Maybe you can use such paradigm to easily train an SNN in your biomed image dataset. Good repos: SnnTorch comes with the best tutorials to explain SNNs and surrogate gradient learning. This is the fastest way to understand the field and begin to implement you solution. Nevertheless, spikingjelly remains a better option when it comes to implement your ideas (better memory efficiency, etc). Good mention to lava-dl, with which you can train a neural network and directly transfer it into neuromorphic hardware (Intel Loihi) if you have access to this kind of chip.
CUDA-Guide
What are some alternatives?
snntorch - Deep and online learning with spiking neural networks in Python
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
norse - Deep learning with spiking neural networks (SNNs) in PyTorch.
cub - [ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl
lava-dl - Deep Learning library for Lava
DOKSparse - sparse DOK tensors on GPU, pytorch
bindsnet - Simulation of spiking neural networks (SNNs) using PyTorch.
LSQR-CUDA - This is a LSQR-CUDA implementation written by Lawrence Ayers under the supervision of Stefan Guthe of the GRIS institute at the Technische Universität Darmstadt. The LSQR library was authored Chris Paige and Michael Saunders.
norse - Deep learning for spiking neural networks
cudnnxx - cuDNN C++ wrapper.
FirstCollisionTimestepRarefiedGasSimulator - This simulator computes all possible intersections for a very small timestep for a particle model
caer - High-performance Vision library in Python. Scale your research, not boilerplate.