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arXiv · 2511.03938

LogHD: Robust Compression of Hyperdimensional Classifiers via Logarithmic Class-Axis Reduction

Sanggeon Yun·Hyunwoo Oh·Ryozo Masukawa·Pietro Mercati·Nathaniel D. Bastian·Mohsen Imani
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Abstract

Hyperdimensional computing (HDC) suits memory, energy, and reliability-constrained systems, yet the standard "one prototype per class" design requires $O(CD)$ memory (with $C$ classes and dimensionality $D$). Prior compaction reduces $D$ (feature axis), improving storage/compute but weakening robustness. We introduce LogHD, a logarithmic class-axis reduction that replaces the $C$ per-class prototypes with $n\!\approx\!\lceil\log_k C\rceil$ bundle hypervectors (alphabet size $k$) and decodes in an $n$-dimensional activation space, cutting memory to $O(D\log_k C)$ while preserving $D$. LogHD uses a capacity-aware codebook and profile-based decoding, and composes with feature-axis sparsification. Across datasets and injected bit flips, LogHD attains competitive accuracy with smaller models and higher resilience at matched memory. Under equal memory, it sustains target accuracy at roughly $2.5$-$3.0\times$ higher bit-flip rates than feature-axis compression; an ASIC instantiation delivers $498\times$ energy efficiency and $62.6\times$ speedup over an AMD Ryzen 9 9950X and $24.3\times$/$6.58\times$ over an NVIDIA RTX 4090, and is $4.06\times$ more energy-efficient and $2.19\times$ faster than a feature-axis HDC ASIC baseline.

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Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa, Pietro Mercati, Nathaniel D. Bastian, Mohsen Imani. 2026-02-03. LogHD: Robust Compression of Hyperdimensional Classifiers via Logarithmic Class-Axis Reduction. https://arxiv.org/abs/2511.03938

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