Spatial architectures provide energy-efficient computation but require effective scheduling algorithms. Existing heuristic-based approaches offer low compiler/architect productivity, little optimality insight, and low architectural portability. We seek to develop a spatial-scheduling framework by utilizing constraint-solving theories and find that architecture primitives and scheduler responsibilities can be related through five abstractions: computation placement, data routing, event timing, resource utilization, and the optimization objective. We encode these responsibilities as 20 mathematical constraints, using SMT and ILP, and create schedulers for the TRIPS, DySER, and PLUG architectures. Our results show that a general declarative approach using constraint solving is implementable, is practical, and can outperform specialized schedulers.

Specialized execution using spatial architectures provides energy efficient computation, but requires effective algorithms for spatially scheduling the computation. Generally, this has been solved with architecture-specific heuristics, an approach which suffers from poor compiler/architect productivity, lack of insight on optimality, and inhibits migration of techniques between architectures.