  Brain-inspired hyperdimensional computing (HDC) is an emerging computational
paradigm that mimics brain cognition and leverages hyperdimensional vectors
with fully distributed holographic representation and (pseudo)randomness.
Compared to other machine learning (ML) methods such as deep neural networks
(DNNs), HDC offers several advantages including high energy efficiency, low
latency, and one-shot learning, making it a promising alternative candidate on
a wide range of applications. However, the reliability and robustness of HDC
models have not been explored yet. In this paper, we design, implement, and
evaluate HDTest to test HDC model by automatically exposing unexpected or
incorrect behaviors under rare inputs. The core idea of HDTest is based on
guided differential fuzz testing. Guided by the distance between query
hypervector and reference hypervector in HDC, HDTest continuously mutates
original inputs to generate new inputs that can trigger incorrect behaviors of
HDC model. Compared to traditional ML testing methods, HDTest does not need to
manually label the original input. Using handwritten digit classification as an
example, we show that HDTest can generate thousands of adversarial inputs with
negligible perturbations that can successfully fool HDC models. On average,
HDTest can generate around 400 adversarial inputs within one minute running on
a commodity computer. Finally, by using the HDTest-generated inputs to retrain
HDC models, we can strengthen the robustness of HDC models. To the best of our
knowledge, this paper presents the first effort in systematically testing this
emerging brain-inspired computational model.
