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：源于大脑的超维计算（HDC）近年来因其在资源受限设备上的优异学习表现备受关注。但是，现有方法使用静态编码器在学习过程中从未更新，这导致需要非常高的维度才能达到足够的准确性，从而严重降低了编码和训练效率。本文提出了一种新颖的超维自适应学习动态编码技术DistHD，有效地识别和重新生成有误导性的维度以确保分类准确性和提高学习质量。我们的算法成功加速了学习进程并在较低维度下实现了期望的准确性。