Low-Complexity Scalable Architectures for Parallel Computation of Similarity Measures

Processor array architectures have been employed, as an accelerator, to compute similarity distance found in a variety of data mining algorithms. However, most of the proposed architectures in the existing literature are designed in an ad hoc manner without taking into consideration the size and dimensionality of the datasets. Furthermore, data dependencies have not been analyzed, and often, only one design choice is considered for the scheduling and mapping of computational tasks. In this work, we present a systematic methodology to design scalable and area-efficient linear (1-D) processor arrays for the computation of similarity distance matrices. Six possible design options are obtained and analyzed in terms of area and time complexities. The obtained architectures provide us with the flexibility to choose the one that meets hardware constraints for a specific problem size. Comparisons with the previously reported architectures demonstrate that one of the proposed architectures achieves less area and area-delay product besides its scalability to high-dimensional data