A new look at exploiting data parallelism in embedded systems

TL;DR: While some algorithms exhibit data-level parallelism suited to packed vector computation, it is shown that other kernels are most efficiently scheduled with more flexible vector models, which motivates exploration of non-traditional processor architectures for the embedded domain.

Abstract: This paper describes and evaluates three architectural methods for accomplishing data parallel computation in a programmable embedded system. Comparisons are made between the well-studied Very Long Instruction Word (VLIW) and Single Instruction Multiple Packed Data (SIMpD) paradigms; the less-common Single Instruction Multiple Disjoint Data (SIMdD) architecture is described and evaluated. A taxonomy is defined for data-level parallel architectures, and patterns of data access for parallel computation are studied, with measurements presented for over 40 essential telecommunication and media kernels. While some algorithms exhibit data-level parallelism suited to packed vector computation, it is shown that other kernels are most efficiently scheduled with more flexible vector models. This motivates exploration of non-traditional processor architectures for the embedded domain.