AI 中文总结
SupHDC通过共享编码叠加处理多个查询，利用高维冗余实现高效推理，在十个数据集上获得最高2.08倍加速且准确率损失极小。
AI 中文摘要
超维计算（HDC）因其高效和鲁棒的学习能力而具有吸引力，但传统推理仍然独立地对每个查询进行编码，反复支付高维投影的成本。我们提出了SupHDC，一种新的推理范式，通过共享的编码计算来处理多个查询。SupHDC分配轻量级随机槽键，在编码前叠加带键的查询，并使用槽特定的分类器来恢复它们的个体预测。随机特征核视图解释了为什么精确恢复每个超向量是不必要的：推理只需要保留决定预测的类别证据。在十个数据集上，SupHDC实现了1.39倍的分析加速，且平均准确率无损失；在仅2.67个百分点的平均准确率损失下，实现了高达2.08倍的加速。在树莓派5上，它实现了2.01倍的实测墙钟加速，平均预测准确率损失2.26个百分点。SupHDC表明，高维冗余不仅可以用于鲁棒性，还可以作为共享推理的容量。
英文摘要
Hyperdimensional computing (HDC) is attractive for efficient and robust learning, but conventional inference still encodes every query independently, repeatedly paying the cost of high-dimensional projection. We introduce SupHDC, a new inference paradigm that processes multiple queries through a shared encoding computation. SupHDC assigns lightweight random slot keys, superposes the keyed queries before encoding, and uses slot-specific classifiers to recover their individual predictions. A random-feature kernel view explains why exact recovery of each hypervector is unnecessary: inference only needs to preserve the class evidence that determines the prediction. Across ten datasets, SupHDC achieves 1.39x analytical speedup with no average accuracy loss, and up to 2.08x speedup with only a 2.67 percentage-point mean accuracy loss. On a Raspberry Pi~5, it delivers 2.01x measured wall-clock speedup with a 2.26 percentage-point loss in mean prediction accuracy. SupHDC shows that high-dimensional redundancy can be used not only for robustness, but also as capacity for shared inference.
