Deep Continuous Local Learning (DECOLLE) with Hessian Aware Quantization

DECOLLE is an online learning framework for spiking neural networks. The algorithmic details are described in this Frontiers paper. If you use this work in your research, please cite as:

@ARTICLE{decolle2020,
AUTHOR={Kaiser, Jacques and Mostafa, Hesham and Neftci, Emre},
TITLE={Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)},
JOURNAL={Frontiers in Neuroscience},
VOLUME={14},
PAGES={424},
YEAR={2020},
URL={https://www.frontiersin.org/article/10.3389/fnins.2020.00424},
DOI={10.3389/fnins.2020.00424},
ISSN={1662-453X}

This repo includes quantization of the network with QPytorch, with bit-precision guided by the layer-wise Hessian trace, as well as a simpliefied neuron model SimpleLIFLayer. More details here. If you use this work in your research, please cite as:

@misc{lui2021hessian,
      title={Hessian Aware Quantization of Spiking Neural Networks}, 
      author={Hin Wai Lui and Emre Neftci},
      year={2021},
      eprint={2104.14117},
      archivePrefix={arXiv},
      primaryClass={cs.NE}
}

Installing

Clone and install. The Python setuptools will take care of dependencies

https://github.com/luithw/decolle-quantization.git
cd decolle-quantization
python setup.py install --user

The following will run decolle on the default parameter set with full precision. This scripts will also compute and printout the layer-wise Hessian trace.

cd scripts
python train_lenet_decolle.py

To enable quantization, add the quantization flag.

python train_lenet_decolle.py --quantization

You can change the bit-precision of each layer by modifying scripts/parameters/params_nmnist_simplelif_full.yml.

Authors

• Emre Neftci - Initial work - eneftci
• Jacques Kaiser - jackokaiser
• Massi Iacono - miacono
• Hin Wai Lui - Hessian Aware Quantization - hwlui

License

This project is licensed under the GPLv3 License - see the LICENSE.txt file for details