提出一种新型超维度计算分类器，显著提升资源受限设备的性能。
Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective
- 基于超维度计算与支持向量机的理论关联，设计最大间隔分类器
- 在多个基准数据集上性能超越传统超维计算方法
- 适合低功耗、嵌入式等硬件受限场景的智能应用
过参数化的机器学习方法（如神经网络）在计算能力有限的设备上可能过于资源密集。超维度计算（HDC）是一种新兴的资源高效、低复杂度的机器学习方法，支持硬件友好的（再）训练与推理实现。本文首次建立了HDC与支持向量机（SVM）之间的形式化联系，并据此提出一种最大间隔HDC分类器，在多个基准数据集上显著优于基线HDC方法。研究结果或可启发更面向硬件优化的HDC新方法，为各类智能资源受限应用场景提供更高效的解决方案。
原文摘要 · Abstract (English)
Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensional computing (HDC) is an emerging resource efficient and low-complexity ML method that allows hardware efficient implementations of (re-)training and inference procedures. In this paper, we propose a maximum-margin HDC classifier, which significantly outperforms baseline HDC methods on several benchmark datasets. Our method leverages a formal relation between HDC and support vector machines (SVMs) that we established for the first time. Our findings may inspire novel HDC methods with potentially more hardware-oriented implementations compared to SVMs, thus enabling more efficient learning solutions for various intelligent resource-constrained applications.
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