  Machine Learning algorithms based on Brain-inspired Hyperdimensional(HD)
computing imitate cognition by exploiting statistical properties of
high-dimensional vector spaces. It is a promising solution for achieving high
energy efficiency in different machine learning tasks, such as classification,
semi-supervised learning, and clustering. A weakness of existing HD
computing-based ML algorithms is the fact that they have to be binarized to
achieve very high energy efficiency. At the same time, binarized models reach
lower classification accuracies. To solve the problem of the trade-off between
energy efficiency and classification accuracy, we propose the QubitHD
algorithm. It stochastically binarizes HD-based algorithms, while maintaining
comparable classification accuracies to their non-binarized counterparts. The
FPGA implementation of QubitHD provides a 65% improvement in terms of energy
efficiency, and a 95% improvement in terms of training time, as compared to
state-of-the-art HD-based ML algorithms. It also outperforms state-of-the-art
low-cost classifiers (such as Binarized Neural Networks) in terms of speed and
energy efficiency by an order of magnitude during training and inference.
