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 on-chip 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 \textit{predefined fusion templates before tensor mapping} and impose \textit{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 \textit{template-free operator fusion after mapping} and supports \textit{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.