Distributed Memory Parallel Architecture Based on Modular Linear Arrays fOr 2-D Separable Transforms Computation

摘要

A framework for mapping systematically 2-dimensional (2-D) separable transforms into a parallel architecture consisting of fully pipelined linear array stages is presented. The resulting model architecture is char- acterized by its generality, high degree of modularity, high throughput, and the exclusive use of distributed memory and control. There is no central shared memory block to facilitate the transposition of intermediate results, as it is commonly the case in row-column image processing architectures. Avoiding shared central memory has positive implications for speed, area, power dissipation and scalability of the architecture. The architecture presented here may be used to realize any separable 2-D transform by only changing the coefficients stored in the processing elements. Pipelined linear arrays for computing the 2-D Discrete Fourier Transform and 2--D separable convolution are presented as examples and their performance is evaluated.