  Inspired by the way human brain works, the emerging hyperdimensional
computing (HDC) is getting more and more attention. HDC is an emerging
computing scheme based on the working mechanism of brain that computes with
deep and abstract patterns of neural activity instead of actual numbers.
Compared with traditional ML algorithms such as DNN, HDC is more
memory-centric, granting it advantages such as relatively smaller model size,
less computation cost, and one-shot learning, making it a promising candidate
in low-cost computing platforms. However, the robustness of HDC models have not
been systematically studied. In this paper, we systematically expose the
unexpected or incorrect behaviors of HDC models by developing HDXplore, a
blackbox differential testing-based framework. We leverage multiple HDC models
with similar functionality as cross-referencing oracles to avoid manual
checking or labeling the original input. We also propose different perturbation
mechanisms in HDXplore. HDXplore automatically finds thousands of incorrect
corner case behaviors of the HDC model. We propose two retraining mechanisms
and using the corner cases generated by HDXplore to retrain the HDC model, we
can improve the model accuracy by up to 9%.
