Brain-inspired hyperdimensional computing (HDC) has been recently considered a promising learning approach for resource-constrained devices. However, existing approaches use static encoders that are never updated during the learning process. Consequently, it requires a very high dimensionality to achieve adequate accuracy, severely lowering the encoding and training efficiency. In this paper, we propose DistHD, a novel dynamic encoding technique for HDC adaptive learning that effectively identifies and regenerates dimensions that mislead the classification and compromise the learning quality. Our proposed algorithm DistHD successfully accelerates the learning process and achieves the desired accuracy with considerably lower dimensionality.

翻译：受脑启发的超维计算（HDC）近年来被视为资源受限设备上一种有前景的学习方法。然而，现有方法采用静态编码器，在学习过程中从不更新。因此，为达到足够的准确性，需要非常高的维度，这严重降低了编码和训练效率。本文提出DistHD，一种用于HDC自适应学习的新型动态编码技术，该技术能有效识别并重构那些误导分类并损害学习质量的维度。我们提出的DistHD算法成功加速了学习过程，并在显著降低维度的条件下达到了期望的准确性。