MEMHD让内存计算架构高效运行高维计算，大幅节省内存与功耗。
MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for Fully-Utilized In-Memory Computing Architectures
- 用聚类初始化+量化感知迭代学习实现多中心记忆存储。
- 相同内存下准确率提升13.69%，或同等精度下内存效率提高13.25倍。
- 适合需要低功耗、高密度部署的边缘智能场景。
在内存计算（IMC）架构上实现高维计算（HDC）面临高维向量与IMC阵列尺寸不匹配的问题，导致内存利用率低且计算周期增加。本文提出MEMHD，一种面向全利用率内存计算架构的高效多中心高维计算框架。MEMHD采用基于聚类的初始化方法与量化感知的迭代学习策略，构建多中心关联记忆。该框架在保持或提升分类准确率的同时，显著降低内存需求。实验表明，MEMHD在相同内存条件下比现有二值化HDC模型最高提升13.69%准确率，或在相同准确率下实现13.25倍内存效率提升；相比基线映射方法，在128×128 IMC阵列上，计算周期减少最多80倍，阵列使用减少最多71倍，同时大幅提升能效与计算效率。
原文摘要 · Abstract (English)
The implementation of Hyperdimensional Computing (HDC) on In-Memory Computing (IMC) architectures faces significant challenges due to the mismatch between highdimensional vectors and IMC array sizes, leading to inefficient memory utilization and increased computation cycles. This paper presents MEMHD, a Memory-Efficient Multi-centroid HDC framework designed to address these challenges. MEMHD introduces a clustering-based initialization method and quantization aware iterative learning for multi-centroid associative memory. Through these approaches and its overall architecture, MEMHD achieves a significant reduction in memory requirements while maintaining or improving classification accuracy. Our approach achieves full utilization of IMC arrays and enables one-shot (or few-shot) associative search. Experimental results demonstrate that MEMHD outperforms state-of-the-art binary HDC models, achieving up to 13.69% higher accuracy with the same memory usage, or 13.25x more memory efficiency at the same accuracy level. Moreover, MEMHD reduces computation cycles by up to 80x and array usage by up to 71x compared to baseline IMC mapping methods when mapped to 128x128 IMC arrays, while significantly improving energy and computation cycle efficiency.
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