论文标题
LEHDC：基于学习的高维计算分类器
LeHDC: Learning-Based Hyperdimensional Computing Classifier
论文作者
论文摘要
多亏了微小的存储和有效的执行，高维计算（HDC）正在成为有关资源约束硬件的轻量级学习框架。但是，现有的HDC培训依赖于各种启发式方法，从而大大限制了它们的推理准确性。在本文中，我们提出了一个名为LEHDC的新型HDC框架，该框架利用了一种有原则的学习方法来提高模型的准确性。具体地，LEHDC将现有的HDC框架映射到了等效的二进制神经网络体系结构中，并采用相应的培训策略来最大程度地减少培训损失。实验验证表明，LEHDC优于先前的HDC培训策略，与基线HDC相比，推理准确性平均可以提高到15％以上。
Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware. Nonetheless, the existing HDC training relies on various heuristic methods, significantly limiting their inference accuracy. In this paper, we propose a new HDC framework, called LeHDC, which leverages a principled learning approach to improve the model accuracy. Concretely, LeHDC maps the existing HDC framework into an equivalent Binary Neural Network architecture, and employs a corresponding training strategy to minimize the training loss. Experimental validation shows that LeHDC outperforms previous HDC training strategies and can improve on average the inference accuracy over 15% compared to the baseline HDC.
