A new hardware architecture of high-performance real-time texture classification system based on FPGA

Yanjun Zhang, Xin Guo, Hongchen Guo*, Yichen Zhang

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

The visual system is essential as a critical source for intelligent robots to acquire external information. Nevertheless, the real-time performance of existing approaches remains inadequate. To address this, a new high-performance target classification system based on FPGA has been developed as part of the visual system. This system optimizes the hardware architecture of the target classification algorithm, incorporating a novel method aimed at boosting parallelism to improve real-time performance. The system is implemented on the Xilinx Zynq-7045 FPGA. Experimental results demonstrate that, for a grayscale image with a resolution of 128 × 128, the feature extraction time is merely 85.64 µs, achieving a speed three orders of magnitude greater than that of the MATLAB platform. Additionally, the resource consumption of this design is lower than that of existing hardware architectures.

源语言英语
文章编号344
期刊Journal of Supercomputing
81
1
DOI
出版状态已出版 - 1月 2025

指纹

探究 'A new hardware architecture of high-performance real-time texture classification system based on FPGA' 的科研主题。它们共同构成独一无二的指纹。

引用此

Zhang, Y., Guo, X., Guo, H., & Zhang, Y. (2025). A new hardware architecture of high-performance real-time texture classification system based on FPGA. Journal of Supercomputing, 81(1), 文章 344. https://doi.org/10.1007/s11227-024-06705-6