Derandomizing Dense Binary Hypervector Codebooks for Quantized Scalars
Hyperdimensional computing and vector symbolic architectures often represent quantized scalar levels by dense binary codebooks whose level-to-level similarity is intended to follow a prescribed function of scalar separation. At finite dimensionality, randomized scalar codebook constructions deviate from this target because of sampling noise, random-start imbalance, update-count fluctuations, component dependence, and finite-capacity effects. We develop a transition-based derandomization framework for dense binary scalar codebooks across two target-similarity families, with similarity decaying exponentially or linearly with level separation. The framework separates the target similarity la...
| Authors | Dmitri Rachkovskij, Evgeny Osipov, Olexander Volkov et al. |
|---|---|
| Primary category | cs.IT |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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