2.3 KiB
pytorch-sbs
SbS Extension for PyTorch
Based on these scientific papers
Back-Propagation Learning in Deep Spike-By-Spike Networks
David Rotermund and Klaus R. Pawelzik
Front. Comput. Neurosci., https://doi.org/10.3389/fncom.2019.00055
https://www.frontiersin.org/articles/10.3389/fncom.2019.00055/full
Efficient Computation Based on Stochastic Spikes
Udo Ernst, David Rotermund, and Klaus Pawelzik
Neural Computation (2007) 19 (5): 1313–1343. https://doi.org/10.1162/neco.2007.19.5.1313
https://direct.mit.edu/neco/article-abstract/19/5/1313/7183/Efficient-Computation-Based-on-Stochastic-Spikes
Python
It was programmed with 3.10.4. And I used some 3.10 Python expression. Thus you might get problems with older Python versions.
C++
You need to modify the Makefile in the C++ directory to your Python installation.
In addition your Python installation needs the PyBind11 package installed. You might want to perform a
pip install pybind11
The Makefile uses clang as a compiler. If you want something else then you need to change the Makefile.
For CUDA I used version 12.0.
Config files and pre-existing weights
Three .json config files are required:
dataset.json : Information about the dataset
network.json : Describes the network architecture
def.json : Controlls the other parameters
If you want to load existing weights, just put them in a sub-folder called Previous
Other relevant scientific papers
NNMF
Learning the parts of objects by non-negative matrix factorization
Lee, Daniel D., and H. Sebastian Seung. Nature 401.6755 (1999): 788-791.
https://doi.org/10.1038/44565
Algorithms for non-negative matrix factorization.
Lee, Daniel, and H. Sebastian Seung. Advances in neural information processing systems 13 (2000).
SbS
Massively Parallel FPGA Hardware for Spike-By-Spike Networks
David Rotermund, Klaus R. Pawelzik
https://doi.org/10.1101/500280
Biologically plausible learning in a deep recurrent spiking network
David Rotermund, Klaus R. Pawelzik
https://doi.org/10.1101/613471
Accelerating Spike-by-Spike Neural Networks on FPGA With Hybrid Custom Floating-Point and Logarithmic Dot-Product Approximation
Yarib Nevarez, David Rotermund, Klaus R. Pawelzik, Alberto Garcia-Ortiz
https://doi.org/10.1109/access.2021.3085216