Multibank Memory Optimization For Parallel Data Access In Multiple Data Arrays

To realize high throughput out of a relatively low bandwidth, memory partitioning algorithms have been proposed to separate data arrays into multiple memory banks, from which multiple data can be accessed in parallel. However, previous partitioning schemes only considered the case of single-pattern and single-array. In this paper, we propose an efficient two-step memory partitioning strategy for multi-pattern access in multiple multi-dimensional arrays. First, a fast, low complexity and low difference-baseddata splitting algorithm provides a multi-bank solution for multiple patterns access. Then an area-efficiency bank merging algorithm reduce the area overhead caused by partitioning. Experimental results show that our memory splitting algorithm saves up to 83.0% in searching time finding a multi-bank solution, compared to the state-of-the-art approach and the storage overhead can be reduced by 34.5%. Meanwhile the area overheads are saved up to 18.86% and the whole partition time are saved up to 45.6% through our entire algorithm.