SFD: Towards Segment Fusion Dataflow for Spatial Accelerators

Spatial accelerators are promising to satiate the growing demands for performance and energy efficiency in deep neural networks (DNNs). Due to the speed gap between onchip compute cores and off-chip memory bandwidth, common DNNs suffer from poor operational intensity and are increasingly memory-bound. While operator fusion has shown potential in alleviating this bottleneck, existing approaches suffer from two key limitations. They rely on predefined fusion templates before tensor mapping and impose tile constraints during mapping. As a result, they overlook the potential of fusing more operators and lead to sub-optimal performance. In this paper, we propose a segment fusion dataflow optimization framework called SFD. Central to this framework is the dataflow abstraction that enables template-free operator fusion after mapping and supports tile constraint relaxation through tile scheduling. Based on this abstraction, we first introduce a memory-centric mapper, which defines a design space and incorporates an algorithm to facilitate design space exploration (DSE). Then we propose an analytical network segmenter, which leverages mapping results to analyze tensor lifetimes and on-chip memory usage, fusing operators into variable-length segments. Finally, we introduce a dependency-aware tile scheduler, which develops a priority queue for each segment to ensure correct execution order. Extensive experiments with different DNNs demonstrate SFD achieves$1.4 \times$to$2.2 \times$speedup for spatial accelerators over state-of-the-art fusion frameworks.