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Fast Neuromimetic Object Recognition Using FPGA Outperforms GPU Implementations

机译:使用FPGA的快速仿神经物体识别性能胜过GPU实现

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Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128$,times,$128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.
机译:传统上,在静止图像中识别对象被认为是一个困难的计算问题。尽管用于视觉对象识别的现代自动化方法已经实现了稳定的识别精度提高,但是即使是最先进的计算视觉方法也无法获得与人类相同的性能。这导致创建了许多受生物启发的视觉对象识别模型,其中包括层次模型和X(HMAX)模型。传统上已知HMAX以显着的计算复杂性为代价在视觉对象识别任务中实现高精度。复杂度的增加又增加了计算时间,减少了每单位时间可以处理的图像数量。在本文中,我们描述了如何修改用于视觉目标识别的计算密集型和受生物学启发的HMAX模型,以便在商业现场可编程Aate Array上实现,特别是在具有XC6VLX240T FPGA的Xilinx Virtex 6 ML605评估板上。我们显示,对传统的HMAX模型进行较小的修改,就可以对大小为128的图像执行识别。 $,times,$ 128像素以每秒190张图像的速度在二进制和多类视觉对象识别任务中的识别精度损失不到1%。

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