{"ID":2845109,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03938","arxiv_id":"2511.03938","title":"LogHD: Robust Compression of Hyperdimensional Classifiers via Logarithmic Class-Axis Reduction","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.","short_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 L...","url_abs":"https://arxiv.org/abs/2511.03938","url_pdf":"https://arxiv.org/pdf/2511.03938v2","authors":"[\"Sanggeon Yun\",\"Hyunwoo Oh\",\"Ryozo Masukawa\",\"Pietro Mercati\",\"Nathaniel D. Bastian\",\"Mohsen Imani\"]","published":"2025-11-06T00:33:21Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}