Carrot-hole Data Scheduling and AdaptivePartitioning for Memory Tra cMinimization

Massive uniform nested loops are broadly used in scientiic and multi-dimensional Digital Signal Processing applications. Due to the amount of data handled by such applications, cache or on-chip memory are required to improve the data access and overall system performance. Most of existing application speciic systems do not eeciently optimize the access to diierent levels of memory hierarchy. In this study, a static data scheduling method, carrot-hole data scheduling, is proposed for multi-dimensional applications represented by multi-dimensional data ow graphs, in order to control the data traac between diierent levels of memory. Based on this data schedule, optimal partitioning and scheduling are selected. The partition size is also chosen in such a way to minimize memory access overhead. Experiments show that by using this technique, on-chip memory misses are signiicantly reduced as compared to results obtained from traditional methods. The carrot-hole data scheduling method is proven to obtain smallest on-chip memory misses compared with other linear scheduling and partitioning schemes.