HyperSpace: A Generalized Framework for Spatial Encoding in Hyperdimensional Representations
For researchers using VSAs, HyperSpace reveals practical system-level trade-offs missed by operator-level analysis, enabling informed backend selection.
HyperSpace is an open-source framework for modular evaluation of Vector Symbolic Architectures. Benchmarking HRR and FHRR backends shows that similarity and cleanup dominate runtime, making end-to-end performance comparable despite FHRR's lower theoretical complexity, while HRR uses half the memory.
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.