Hyperdimensional computing (HDC) uses binary vectors of high dimensions to perform classification. Due to its simplicity and massive parallelism, HDC can be highly energy-efficient and well-suited for resource-constrained platforms. However, in trading off orthogonality with efficiency, hypervectors may use tens of thousands of dimensions. In this paper, we will examine the necessity for such high dimensions. In particular, we give a detailed theoretical analysis of the relationship among dimensions of hypervectors, accuracy, and orthogonality. The main conclusion of this study is that a much lower dimension, typically less than 100, can also achieve similar or even higher detecting accuracy compared with other state-of-the-art HDC models. Based on this insight, we propose a suite of novel techniques to build HDC models that use binary hypervectors of dimensions that are orders of magnitude smaller than those found in the state-of-the-art HDC models, yet yield equivalent or even improved accuracy and efficiency. For image classification, we achieved an HDC accuracy of 96.88\% with a dimension of only 32 on the MNIST dataset. We further explore our methods on more complex datasets like CIFAR-10 and show the limits of HDC computing.
翻译:超元计算(HDC)使用高维量的二进制矢量进行分类。由于它的简单性和大规模平行性,HDC可以是高能效的,并且完全适合资源限制平台。然而,在以效率来交换正反正方位时,高压者可能会使用数万个维度。在本文件中,我们将研究这种高维度的必要性。特别是,我们将对超维量体、准确度和正方位的维度之间的关系进行详细的理论分析。本研究的主要结论是,低得多的维度(通常不到100个)也可以与其他最先进的HDC模型相比达到类似甚至更高的检测准确度。基于这一洞察,我们提出一套新技术来建立HDC模型,这些模型使用比最新高维度的二进制高维度,其尺寸比在HDC模型中发现的要小,但所产生的准确性甚至更高。关于图像分类,我们实现了96.88%的HDC精确度,比其他最先进的HDC模型的尺寸只有32个。我们进一步探索了我们有关FAR-MIS-10数据限制的数据。