Multi-Model Inference Composition of Hyperdimensional Computing Ensembles

To answer the ever-increasing demand for high accuracy in artificial intelligence (AI)-based applications, several models have been proposed. Among them, ensemble learning, a technique that trains multiple classifiers and then combines their prediction during the inference stage, emerged as a promising approach. Despite being largely explored in the context of models like random forests or convolutional neural networks, very few research works have focused on ensemble learning targeting hyperdimensional computing (HDC). HDC is a brain-inspired computing paradigm that has gained momentum in the last decade because its lightweight and highly parallel operations make it an excellent alternative to compute-intense deep learning models for edge AI applications. In this work, we propose BagHD and BoostHD, two ensemble-based HDC implementations constructed using bagging and boosting, respectively. Accuracy evaluations indicate that our proposal improves baseline single-instance implementations and state-of-the-art HDC ensembles by up to 14% and 4%, respectively. We then leverage two key characteristics of HDC and ensemble learning to demonstrate how we can transform the proposed ensembles into equivalent single-instance implemen-tations, thus avoiding any memory and computing overhead during inference. In fact, when compared to traditional ordinary ensembles, we reduce memory requirements by up to 40x, improving accuracy at the same time. We also support ensemble learning HDC training in BagHD and BoostHD, showing that with little memory overhead it is possible to retrieve the original weak learners from the generated single-instance design.