Understanding Hyperdimensional Computing for Parallel Single-Pass Learning

Authors:

*: Equal Contribution

Introduction

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

Dependencies and Data

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.

Usage

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

Citation

If you find this repo useful, please cite:

Understanding Hyperdimensional Computing for Parallel Single-Pass Learning