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首页> 外文会议>2014 International Symposium on Optomechatronic Technologies >Performance Prediction of Visual Algorithms on Different Hardware Architectures
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Performance Prediction of Visual Algorithms on Different Hardware Architectures

机译：视觉算法在不同硬件体系结构上的性能预测

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摘要

In many specialties related to computer vision, such as video processing for object inspection, microscopy or video surveillance, a performance requirement arises, the choice of hardware architecture that sized well is crucial for experts in image processing. However, these experts are not always expert in hardware architecture and are not always able to estimate a priori the performance of their algorithms on a given architecture. Here, we propose the first elements of a tool that will be able to predict the performance, in term of time, of a given vision algorithm. Convolution is studied as an unavoidable algorithm in computer vision. We designed some benchmarks where different parameters related to the convolution where studied: size of the filter, size of the image, parallelism. Our results showed that it is possible with a correct accuracy to obtain a prediction of the timing of the convolution with three different architectures with a limited number of parameters. In future work, other algorithmic building blocks will be tested to validate our approach on a complex algorithm that will be a combination of identified building blocks. We want to propose an algorithmic language for image processing that applies to hardware performance.
机译：在许多与计算机视觉相关的专业中，例如用于对象检查，显微镜检查或视频监视的视频处理中，会出现性能要求，因此选择合适大小的硬件体系结构对于图像处理专家至关重要。但是，这些专家并不总是硬件体系结构方面的专家，也不总是能够先验地估计其算法在给定体系结构上的性能。在这里，我们提出了工具的第一个元素，该元素将能够在时间方面预测给定视觉算法的性能。卷积是计算机视觉中不可避免的算法。我们设计了一些基准，其中研究了与卷积相关的不同参数：滤波器的大小，图像的大小，并行度。我们的结果表明，有可能以正确的精度使用有限数量的参数的三种不同体系结构来获得卷积时序的预测。在以后的工作中，将测试其他算法构造块，以验证我们将采用复杂算法的方法，该算法将是已识别构造块的组合。我们想提出一种适用于硬件性能的图像处理算法语言。

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