Performance Analysis of Hyperdimensional Computing for Character Recognition
Performance Analysis of Hyperdimensional Computing for Character Recognition
复制标题
字符识别超维计算性能分析
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Alvarez
中科院分区：
文献类型：
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作者：
Alec Xavier Manabat;Celine Rose Marcelo;Alfonso Louis Quinquito;A. Alvarez
物联网（IoT）是一个不断扩展的设备网络。物联网的整个前提是提供舒适和便利，需要识别等功能。这是通过数据完成的-首先由节点收集，然后处理以提供有意义的信息，通常使用机器学习。然而，传统的机器学习技术（例如深度学习）需要复杂的操作，这对于小型设备来说可能是计算繁重的。本研究探讨了多维计算（HDC）的能力，作为一个不太复杂的数据分类架构。通过一个字符识别应用程序，对HDC性能中的维度（以及因此的能量）的影响进行了探索。结果表明，4,000位的一次性学习系统的维数产生的平均准确率接近其12,000位的对应在0%的失真，和平均准确率为89.94%，失真为14.29%。这项研究为HDC应用程序的优化提供了见解。
The Internet of Things (IoT) is an increasingly expanding network of devices. The whole premise of IoT is to provide comfort and convenience, requiring functions such as recognition. This is done through data - first collected by nodes, and then processed to provide meaningful information, commonly using machine learning. However, conventional machine learning techniques such as deep-learning require complicated operations, which may be computationally heavy for small devices. This study explores the capabilities of hyperdimensional computing (HDC), as a less complex architecture for data classification. The effect of dimensionality (and therefore energy) in the performance of HDC, done through a character recognition application, was explored. Results show that a dimensionality of 4,000-bit one-shot learning system yields an average accuracy close to that of its 12,000-bit counterpart at 0% distortion, and an average accuracy of 89.94% with 14.29% distortion. This study provides insights towards optimizations of HDC applications.