提出可模块化评估的超维度空间编码框架，揭示实际性能与理论复杂度的差异。
HyperSpace: A Generalized Framework for Spatial Encoding in Hyperdimensional Representations
- 将超维度表示分解为编码、绑定、打包等模块化操作，支持系统级分析。
- 实测显示相似度和清理操作主导运行时间，使HRR与FHRR端到端性能接近。
- 适合关注超维计算部署优化的研究者或工程团队参考。
向量符号架构（VSAs）为高维空间中的组合表示提供了明确的代数框架。本文提出HyperSpace——一个开源框架，将VSA系统拆解为编码、绑定、打包、相似性、清理和回归等模块化算子。利用该框架，我们分析并基准测试了两种代表性VSA后端：全息缩减表示（HRR）和傅里叶全息缩减表示（FHRR）。尽管FHRR在单个操作上具有更低的理论复杂度，但模块化分析表明，在空间领域中，相似性和清理操作主导了运行时间。因此，HRR与FHRR表现出相近的端到端性能。内存占用差异带来额外部署权衡：HRR向量所需内存约为FHRR的一半。通过支持模块化、系统级评估，HyperSpace揭示了仅从理论或算子层面无法察觉的实际权衡。
原文摘要 · Abstract (English)
Vector Symbolic Architectures (VSAs) provide a well-defined algebraic framework for compositional representations in hyperdimensional spaces. We introduce HyperSpace, an open-source framework that decomposes VSA systems into modular operators for encoding, binding, bundling, similarity, cleanup, and regression. Using HyperSpace, we analyze and benchmark two representative VSA backends: Holographic Reduced Representations (HRR) and Fourier Holographic Reduced Representations (FHRR). Although FHRR provides lower theoretical complexity for individual operations, HyperSpaces modularity reveals that similarity and cleanup dominate runtime in spatial domains. As a result, HRR and FHRR exhibit comparable end-to-end performance. Differences in memory footprint introduce additional deployment trade-offs where HRR requires approximately half the memory of FHRR vectors. By enabling modular, system-level evaluation, HyperSpace reveals practical trade-offs in VSA pipelines that are not apparent from theoretical or operator-level comparisons alone.
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