Optimizing Memory Allocation for Multi-Subgraph Mapping on Spatial Accelerators

Abstract

Spatial accelerators enable the pervasive use of energy-efficient solutions for computation-intensive applications. In the mapping of spatial accelerators, a large kernel is usually partitioned into multiple subgraphs for resource constraints, leading to more memory accesses and access conflicts. To minimize the access conflicts, existing works either neglect the interference of multiple subgraphs or pay little attention to data's life cycle along the execution order. To this end, this paper proposes an optimized memory allocation approach for multi-subgraph mapping on spatial accelerators by constructing an optimization problem using Integer Linear Programming (ILP). The experimental results demonstrate that our work can find conflict-free solutions for most kernels and achieve 1.15× speedup, as compared to the state-of-the-art approach.