Achraf El Bouazzaoui, Noura Jariri, Omar Mouhib, Abdelkader Hadjoudja
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引用次数: 0
Abstract
This study introduces an Adaptive K-Nearest Neighbors methodology designed for FPGA platforms, offering substantial improvements over traditional K-Nearest Neighbors implementations. By integrating a dynamic classifier selection system, our approach enhances adaptability, enabling on-the-fly adjustments of K values and subsets of training data. This flexibility results in up to a 10.66% improvement in accuracy and significantly reduces latency, rendering our system up to 3.918 times more efficient than conventional K-Nearest Neighbors techniques. The methodology’s efficacy is validated through experiments across multiple datasets, demonstrating its potential in optimizing both classification accuracy and system efficiency. The adaptive approach’s ability to improve response times, along with its flexibility, positions it as an ideal solution for real-time applications and highlights the advantages of the adaptive K-Nearest Neighbors methodology in overcoming the constraints of hardware-accelerated machine learning.
本研究介绍了一种专为 FPGA 平台设计的自适应 K 近邻方法,与传统的 K 近邻实现方法相比有了很大改进。通过集成动态分类器选择系统,我们的方法增强了适应性,可对 K 值和训练数据子集进行即时调整。这种灵活性使准确率提高了 10.66%,并显著降低了延迟,使我们系统的效率是传统 K 近邻技术的 3.918 倍。该方法的功效通过多个数据集的实验得到了验证,证明了它在优化分类准确性和系统效率方面的潜力。自适应方法能够改善响应时间,而且具有灵活性,这使它成为实时应用的理想解决方案,并凸显了自适应 K 近邻方法在克服硬件加速机器学习限制方面的优势。
期刊介绍:
In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.