*: Equal Contribution
This repo contains implementation of the group VSA and binary HDC model with random Fourier feature (RFF) encoding, described in the paper Understanding Hyperdimensional Computing for Parallel Single-Pass Learning.
Our RFF method and group VSA can outperform the state-of-the-art HDC model while maintaining hardware efficiency. For example, on MNIST,
| Model | 1-Epoch Accuracy | 10-Epoch Accuracy | Circuit-Depth Complexity |
|---|---|---|---|
| Percep. | 94.3 % | 94.3 % | 1299 |
| SOTA HDC | NA | 89.0 % | 295 |
| RFF HDC | 95.4 % | 95.4 % | 295 |
| RFF G(2^3)-VSA | 96.3 % | 95.7 % | 405 |
Numpy and PyTorch>=1.0.0 are required to run the implementation. Supported datasets include MNIST, Fashion-MNIST, CIFAR-10, ISOLET and UCI-HAR. We provide the ISOLET and UCI-HAR data in dataset folder.
Please create the ./encoded_data folder before running the following code.
$ python main.py [-h] [-lr LR] [-gamma GAMMA] [-epoch EPOCH] [-gorder GORDER] [-dim DIM]
[-data_dir DATA_DIR] [-model MODEL]
optional arguments:
-h, --help show this help message and exit
-lr LR learning rate for optimizing class representative
-gamma GAMMA kernel parameter for computing covariance
-epoch EPOCH epochs of training
-gorder GORDER order of the cyclic group required for G-VSA
-dim DIM dimension of hypervectors
-resume resume from existing encoded hypervectors
-data_dir DATA_DIR Directory used to save encoded data (hypervectors)
-dataset {mnist,fmnist,cifar,isolet,ucihar}
dataset (mnist | fmnist | cifar | isolet | ucihar)
-raw_data_dir RAW_DATA_DIR
Raw data directory to the dataset
-model {rff-hdc,linear-hdc,rff-gvsa}
feature and model to use: (rff-hdc | linear-hdc | rff-gvsa)
For example,
$ python main.py -gamma 0.3 -epoch 10 -gorder 8 -dim 10000 -dataset mnist -model rff-gvsa
If you find this repo useful, please cite:
@article{yu2022understanding,
title={Understanding Hyperdimensional Computing for Parallel Single-Pass Learning},
author={Tao Yu and Yichi Zhang and Zhiru Zhang and Christopher De Sa},
year={2022},
eprint={2202.04805},
archivePrefix={arXiv},
primaryClass={cs.LG}
}