A Computing Architecture of Adjustable Convolution
System for Image Processing
Po-Ning Chen*, Yung-Sheng Chen* and Wen-Hsing Hsu*,**
*Institute of Electrical Engineering,
National Tsing Hua University,
**Institute of Information Science,
This paper describes a design of adjustable convolutional hardware in image processing. As performing an n¡Ñn convolution traditionally needs n2 processing elements (PEs), therefore, the larger the mask size is, the more the number of PEs and the cost are. In order to achieve a reasonable performance/cost ratio, we design a new architecture using n or less PEs to perform an n¡Ñn convolution. This hardware architecture together with a few delay-line buffers is designed in pipeline-and-paralle1 fashion. Presently, a 3¡Ñ3 convolutional prototype consisting of only two PEs has been constructed on a single board and can approximately operate in real-time.