XL-HD: Extended Learning in Hyperdimensional Computing via Deterministic Projections for In-Memory Accelerators
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911713746485248 |
|---|---|
| author | Moon, Sabrina Hassan Masum, Abu Kaisar Mohammad Aygun, Sercan Reis, Dayane |
| author_facet | Moon, Sabrina Hassan Masum, Abu Kaisar Mohammad Aygun, Sercan Reis, Dayane |
| contents | 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 $0.40 \ μ\text{J}$ per single-cycle inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24788 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | XL-HD: Extended Learning in Hyperdimensional Computing via Deterministic Projections for In-Memory Accelerators Moon, Sabrina Hassan Masum, Abu Kaisar Mohammad Aygun, Sercan Reis, Dayane Hardware Architecture Emerging Technologies 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 $0.40 \ μ\text{J}$ per single-cycle inference. |
| title | XL-HD: Extended Learning in Hyperdimensional Computing via Deterministic Projections for In-Memory Accelerators |
| topic | Hardware Architecture Emerging Technologies |
| url | https://arxiv.org/abs/2605.24788 |