Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
Authors: Tao Yu, Yichi Zhang, Zhiru Zhang, Christopher M. De Sa
NeurIPS 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our RFF method and group VSA can both outperform the state-of-the-art HDC model by up to 7.6% while maintaining hardware efficiency. |
| Researcher Affiliation | Academia | Tao Yu Cornell University EMAIL Yichi Zhang Cornell University EMAIL Zhiru Zhang Cornell University EMAIL Christopher De Sa Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1 Construct correlated hypervectors input: similarity matrix M Rn n, dimension d let ˆΣ = sin( π 2 M) {elementwise} let UΛU T = ˆΣ {symmetric eigendecomposition} sample X Rn d iid unit Gaussians return sgn(UΛ1/2 + X) {elementwise} |
| Open Source Code | Yes | Our code is available on github https://github.com/Cornell-Relax ML/Hyperdimensional-Computing. |
| Open Datasets | Yes | We evaluate the performance of proposed methods on two conventional HDC datasets, ISOLET [Dua and Graff, 2017] and UCIHAR [Anguita et al., 2012]. We also evaluate our method on MNIST and Fashion-MNIST [Xiao et al., 2017]. The datasplit is default to each dataset. |
| Dataset Splits | Yes | Datasets. We evaluate the performance of proposed methods on two conventional HDC datasets, ISOLET [Dua and Graff, 2017] and UCIHAR [Anguita et al., 2012]. We also evaluate our method on MNIST and Fashion-MNIST [Xiao et al., 2017]. The datasplit is default to each dataset. |
| Hardware Specification | Yes | We train on Intel Xeon CPUs. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for dependencies. |
| Experiment Setup | Yes | Setups. For ISOLET and UCIHAR, we quantize the features to 8 bits before encoding. We initialize a 10,000-dimensional basis hypervector for each {0, , 255} feature value, then encode raw inputs as described in Section 5 or 6. During the training stage, we use a learning rate of 0.01 and train classifiers for 10 epochs. |