Abstract:Hyperdimensional computing (HDC) is a promising approach for energy-efficient edge machine learning (ML), where low latency, low power, and tight memory budgets are essential. However, traditional HDC relies on symbolic binding and pseudo-random high-dimensional vectors, which require large dimensionality and heuristic updates to reach competitive accuracy, limiting deployment on edge hardware. We introduce XL-HD, a deterministic, projection-based, fully learnable HDC framework tailored for in-memory acceleration within edge computing systems. The method uses a fixed Sobol sequence to project binary inputs, extending learning beyond conventional HDC. During training, class prototypes are optimized in real-valued space and later binarized, enabling an entirely binary dot-product inference pipeline ideal for IMC hardware such as ReRAM crossbars. XL-HD achieves competitive accuracy on MNIST, UCIHAR, and ISOLET while maintaining a compact IMC-based inference engine with $0.395 \ \text{mm}^2$ area and only $0.40 \ \mu\text{J}$ per single-cycle inference.
| Comments: | Accepted at The International Symposium on Low Power Electronics and Design (ISLPED) 2026 |
| Subjects: | Hardware Architecture (cs.AR); Emerging Technologies (cs.ET) |
| Cite as: | arXiv:2605.24788 [cs.AR] |
| (or arXiv:2605.24788v1 [cs.AR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24788 arXiv-issued DOI via DataCite (pending registration) |
From: Sabrina Hassan Moon [view email]
[v1]
Sun, 24 May 2026 00:23:29 UTC (5,673 KB)
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