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Bandwidth Adaptive Hardware Architecture of K-Means Clustering for Video Analysis

机译:用于视频分析的K均值聚类的带宽自适应硬件体系结构

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摘要

K-Means is a clustering algorithm that is widely applied in many fields, including pattern classification and multimedia analysis. Due to real-time requirements and computational-cost constraints in embedded systems, it is necessary to accelerate K-Means algorithm by hardware implementations in SoC environments, where the bandwidth of the system bus is strictly limited. In this paper, a bandwidth adaptive hardware architecture of K-Means clustering is proposed. Experiments show that the proposed hardware can be used in applications such as image segmentation, and it has the maximum clock speed 400-MHz and 440-K gate count with TSMC 90-nm technology. Moreover, the throughput of the proposed hardware reaches 16 dimension/cycle, and it can deal with feature vectors with different dimensions using five parallel modes to utilize the input bandwidth efficiently.
机译:K-Means是一种聚类算法,已广泛应用于许多领域,包括模式分类和多媒体分析。由于嵌入式系统的实时要求和计算成本的限制,有必要通过SoC环境中的硬件实现来加速K-Means算法,在SoC环境中,系统总线的带宽受到严格限制。本文提出了一种K-Means聚类的带宽自适应硬件架构。实验表明,所提出的硬件可用于图像分割等应用,采用台积电90纳米技术,其最大时钟速度为400-MHz,门计数为440-K。此外,所提出的硬件的吞吐量达到16维/周期,并且它可以使用五种并行模式处理具有不同维的特征向量,以有效地利用输入带宽。

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