Wavefront Skipping using BRAMs for Conditional Algorithms on Vector Processors

Abstract

Soft vector processors can accelerate data parallel algorithms on FPGAs while retaining software programmability. To handle divergent control flow, vector processors typically use mask registers and predicated instructions. These work by executing all branches and finally selecting the correct one. Our work improves FPGA based vector processors by adding wavefront skipping, where wavefronts that are completely masked off are skipped. This accelerates conditional algorithms, particularly useful where elements terminate early if simple tests fail but require extensive processing in the worst case. The difference in logic speed and RAM area for FPGA based circuits versus ASICs led us to a different implementation than used in fixed vector processors, storing wavefront offsets in on-chip BRAM rather than computing wavefronts skipped dynamically. Additionally, we allow for partitioning the wavefronts so that partial wavefronts can skip independently of one another. We show that <5% extra area can give up to 3.2× better performance on conditional algorithms. Partial wavefront skipping may not be generally useful enough to be added to a fixed vector processor; it provides up to 65% more performance for up to 27% more area. In an FGPA, however, the designer can use it to make application specific tradeoffs between area and performance.