THDC: Training Hyperdimensional Computing Models with Backpropagation

Hanne Dejonghe; Sam Leroux

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arXiv · 2602.00116

THDC: Training Hyperdimensional Computing Models with Backpropagation

Hanne Dejonghe·Sam Leroux

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Abstract

Hyperdimensional computing (HDC) offers lightweight learning for energy-constrained devices by encoding data into high-dimensional vectors. However, its reliance on ultra-high dimensionality and static, randomly initialized hypervectors limits memory efficiency and learning capacity. Therefore, we propose Trainable Hyperdimensional Computing (THDC), which enables end-to-end HDC via backpropagation. THDC replaces randomly initialized vectors with trainable embeddings and introduces a one-layer binary neural network to optimize class representations. Evaluated on MNIST, Fashion-MNIST and CIFAR-10, THDC achieves equal or better accuracy than state-of-the-art HDC, with dimensionality reduced from 10.000 to 64.

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Hanne Dejonghe, Sam Leroux. 2026-01-27. THDC: Training Hyperdimensional Computing Models with Backpropagation. https://arxiv.org/abs/2602.00116

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