  This paper represents the first effort to explore an automated architecture
search for hyperdimensional computing (HDC), a type of brain-inspired neural
network. Currently, HDC design is largely carried out in an
application-specific ad-hoc manner, which significantly limits its application.
Furthermore, the approach leads to inferior accuracy and efficiency, which
suggests that HDC cannot perform competitively against deep neural networks.
Herein, we present a thorough study to formulate an HDC architecture search
space. On top of the search space, we apply reinforcement-learning to
automatically explore the HDC architectures. The searched HDC architectures
show competitive performance on case studies involving a drug discovery dataset
and a language recognition task. On the Clintox dataset, which tries to learn
features from developed drugs that passed/failed clinical trials for toxicity
reasons, the searched HDC architecture obtains the state-of-the-art ROC-AUC
scores, which are 0.80% higher than the manually designed HDC and 9.75% higher
than conventional neural networks. Similar results are achieved on the language
recognition task, with 1.27% higher performance than conventional methods.
