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BoolNet: Minimizing the Energy Consumption of Binary Neural Networks

This is the accompanying code for our paper BoolNet: Minimizing the Energy Consumption of Binary Neural Networks. Here we provide the code and training details for our trainings from Section 4.5 "Comparison to State-of-the-Art BNNs".

BoolNet Design

Setup

In any case you need to have a CUDA setup with a recent NVIDIA driver on your host system. (We tested driver version 465.27 with support for CUDA 11.3, but drivers supporting CUDA 11.1 should be fine.)

Further you need to download and prepare the ImageNet dataset (how?).

We use enroot to provide an independent environment, but you can install it directly on your machine instead.

With Enroot

(Last tested on: 2021-06-11)

Install enroot and libnvidia-container (for GPU support).

  1. Import pytorch docker image into enroot and install the requirements.
enroot import -o pytorch.sqsh docker://pytorch/pytorch:1.8.1-cuda11.1-cudnn8-runtime
enroot create pytorch.sqsh
enroot start --root --rw -m ".":"/workspace" -- pytorch ./install.sh
  1. (Optional) Repack the modified image (to have a clean image stored):
enroot export --output boolnet.sqsh pytorch
enroot remove pytorch && rm pytorch.sqsh
enroot create boolnet.sqsh
  1. Select the experiment and run the corresponding training command (replace boolnet with pytorch if you did not do the previous step):
cd BaseNet_k_1
cd src
enroot start --rw -m "/path/to/imagenet/data":"/mnt/imagenet" -m ".":"/workspace" -e PYTHONPATH=/workspace -e PYTHONUNBUFFERED=x -- boolnet ./run.sh

Direct Install

Please check our script ./install.sh (designed for Ubuntu systems) to install the requirements needed (in addition to the previously mentioned CUDA installation). Please adapt the script accordingly for other operating systems (or use enroot instead).

After you have installed all requirements, simply copy and run the training command and add --imagenet_directory /path/to/imagenet:

cd BaseNet_k_1
cd src
cat run.sh # print the training command for copying
# paste the command, add the imagenet directory (--imagenet_directory ...) and run

Experiment details

We provide more details about our individual runs (log files, accuracy curves, etc.), in the corresponding directories (or you can just use the links below):

Method Bit-width (W/A/F) Energy Usage ImageNet Accuracy Theoretical Operations
BoolNet*(k=4) 1/1/4 1.18mJ 59.6% 1.76 x 10^8
BoolNet(k=4) 1/1/4 0.84mJ 57.6% 1.64 x 10^8
BaseNet(k=4) 1/1/4 0.74mJ 55.1% 1.54 x 10^8
BaseNet(k=1) 1/1/1 0.61mJ 48.9% 1.51 x 10^8

The bit-width refers to: W = weights, A = inputs (before convolutions), F = feature maps. The details for the calculation of energy usage can be found in our paper.

Contributing

Please feel free to open an issue or a pull request, if you encounter problems or want to provide suggestions.

Cite

If you want to compare to our results or if our code has helped your research, we would be happy if you can cite us:

@misc{guo2021boolnet,
      title={BoolNet: Minimizing The Energy Consumption of Binary Neural Networks}, 
      author={Nianhui Guo and Joseph Bethge and Haojin Yang and Kai Zhong and Xuefei Ning and Christoph Meinel and Yu Wang},
      year={2021},
      eprint={2106.06991},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}