标题： FactorHD：一种用于多目标多类表示和分解的超维计算模型 显示英文标题

标题： FactorHD: A Hyperdimensional Computing Model for Multi-Object Multi-Class Representation and Factorization

摘要： 神经符号人工智能（神经符号AI）在逻辑分析和推理方面表现出色。 超维计算（HDC），一种有前途的受大脑启发的计算模型，是神经符号AI的重要组成部分。 各种HDC模型已被提出用于表示类-实例和类-类关系，但在表示更复杂的类-子类关系时，即多个对象关联不同层次的类和子类时，它们在因子分解方面面临挑战，这是神经符号AI系统的关键任务。 在本文中，我们提出了FactorHD，一种新型的HDC模型，能够高效地表示和因子分解复杂的类-子类关系。 FactorHD具有一种符号编码方法，嵌入了一个额外的记忆条款，保留了更多关于多个对象的信息。 此外，它采用了一种高效的因子分解算法，通过识别目标类的记忆条款选择性地消除冗余类。 这种模型显著提高了在表示和因子分解具有类-子类关系的多个对象时的计算效率和准确性，克服了现有HDC模型的局限性，如“叠加灾难”和“问题2”。 评估结果显示，与现有的HDC模型相比，FactorHD在表示大小为10^9时实现了大约5667倍的速度提升。 当与ResNet-18神经网络集成时，FactorHD在Cifar-10数据集上实现了92.48%的因子分解准确率。 显示英文摘要

摘要： Neuro-symbolic artificial intelligence (neuro-symbolic AI) excels in logical analysis and reasoning. Hyperdimensional Computing (HDC), a promising brain-inspired computational model, is integral to neuro-symbolic AI. Various HDC models have been proposed to represent class-instance and class-class relations, but when representing the more complex class-subclass relation, where multiple objects associate different levels of classes and subclasses, they face challenges for factorization, a crucial task for neuro-symbolic AI systems. In this article, we propose FactorHD, a novel HDC model capable of representing and factorizing the complex class-subclass relation efficiently. FactorHD features a symbolic encoding method that embeds an extra memorization clause, preserving more information for multiple objects. In addition, it employs an efficient factorization algorithm that selectively eliminates redundant classes by identifying the memorization clause of the target class. Such model significantly enhances computing efficiency and accuracy in representing and factorizing multiple objects with class-subclass relation, overcoming limitations of existing HDC models such as "superposition catastrophe" and "the problem of 2". Evaluations show that FactorHD achieves approximately 5667x speedup at a representation size of 10^9 compared to existing HDC models. When integrated with the ResNet-18 neural network, FactorHD achieves 92.48% factorization accuracy on the Cifar-10 dataset.