DATE ’25 MEMHD
MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for Fully-Utilized In-Memory Computing Architectures
Replacing one vector per class with many, so hyperdimensional inference fills an in-memory computing array and finishes in a single cycle.
Specifications
As reported in the paper| Accuracy at equal memory | up to +13.69% |
|---|---|
| Memory efficiency | up to 13.25× |
| Inference | Single cycle, fully utilized array |
Abstract
DATE ’25A memory-efficient multi-centroid hyperdimensional computing framework that replaces single-vector-per-class with multiple class vectors, achieving full IMC array utilization and enabling single-cycle inference through clustering-based initialization and quantization-aware training.
† Co-corresponding authors: K. E. Jeon and J. H. Ko.
Cite
@inproceedings{kang2025memhd,
title = {{MEMHD}: Memory-Efficient Multi-Centroid Hyperdimensional Computing for Fully-Utilized In-Memory Computing Architectures},
author = {Kang, Do Yeong and Oh, Yeong Hwan and Hwang, Chanwook and Kim, Jinhee and Jeon, Kang Eun and Ko, Jong Hwan},
booktitle = {Proceedings of the 2025 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)},
location = {Lyon, France},
year = {2025},
pages = {1--7},
doi = {10.23919/DATE64628.2025.10993253},
}