文章基本信息

标题：A Rotation-based Data Buffering Architecture for Convolution Filtering in a Field Programmable Gate Array
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作者：Lu, Zhijian ; Wu, Yanxia ; Guo, Zhenhua 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2013
卷号：8
期号：6
页码：1411-1416
DOI：10.4304/jcp.8.6.1411-1416
语种：English
出版社：Academy Publisher
摘要：Convolution filtering applications range from image recognition and video surveillance. Two observations drive the design of a new buffering architecture for convolution filters. First, the convolutional operations are inherently local; hence every pixel of the output feature maps is calculated by the neighboring pixels of the input feature maps. Even though the operation is simple, the convolution filtering is both computation-intensive and memory-intensive. For real-time applications, large amounts of on-chip memories are required to support massively parallel processing architectures. Second, to avoid access to external memories directly, the data that are already stored in on-chip memories should be used as many times as possible. Based on these two observations, we show that for a given throughput rate and off-chip memory bandwidth, a rotation-based data buffering architecture provide the optimum area-utilization results for a particular design point, which are commonly used applications in recognition area.
关键词：convolution filtering;Field Programmable Gate Arrays (FPGAs);data buffering