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A hardware implementation of Bag of Words and Simhash for image recognition

机译:用于图像识别的单词和Simhash的硬件实现

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Algorithms such as Bag of Words and Simhash have been widely used in image recognition. To achieve better performance as well as energy-efficiency, a hardware implementation of these two algorithms is proposed in this paper. To the best of our knowledge, it is the first time that these algorithms have been implemented on hardware for image recognition purpose. The proposed implementation is able to generate a fingerprint of an image and find the closest match in the database accurately. It is implemented on Xilinx's Virtex-6 SX475T FPGA. Tradeoffs between high performance and low hardware overhead are obtained through proper parallelization. The experimental result shows that the proposed implementation can process 1,018 images per second, approximately 17.8x faster than software on Intel's 12-thread Xeon X5650 processor. On the other hand, the power consumption is 0.35x compared to software-based implementation. Thus, the overall advantage in energy-efficiency is as much as 46x. The proposed architecture is scalable, and is able to meet various requirements of image recognition.
机译:诸如单词和辛库的袋等算法已广泛用于图像识别。为了实现更好的性能以及节能,本文提出了这两种算法的硬件实现。据我们所知,这是第一次在用于图像识别目的的硬件上实现了这些算法。建议的实施能够生成图像的指纹,并准确地找到数据库中最接近的匹配。它是在Xilinx的Virtex-6 SX475T FPGA上实现的。通过适当的并行化获得高性能和低硬件开销之间的权衡。实验结果表明,建议的实施可以每秒处理1,018张图像,比英特尔12线索Xeon X5650处理器上的软件快约17.8倍。另一方面,与基于软件的实现相比,功耗为0.35倍。因此,能量效率的总体优势高达46倍。所提出的架构是可扩展的,并且能够满足图像识别的各种要求。

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