Crane: Inter-Layer Scheduling Framework for DNN Inference and Training Co-Support on Tiled Architecture

Tiled architectures have emerged as a compelling platform for scaling deep neural network (DNN) execution, offering both compute density and communication efficiency. To harness their full potential, effective inter-layer scheduling is crucial for managing operation order, memory behavior, and compute resource coordination. However, current schedulers often fall short due to three persistent issues: incomplete treatment of core design factors, limited flexibility in handling diverse workload structures, and reliance on heuristic search algorithms with poor convergence. In this work, we trace these limitations to the absence of a unified and expressive scheduling representation. We introduce Crane, a framework that addresses these gaps through a hierarchical table-format abstraction capable of encoding rich scheduling semantics. Crane supports both inference and training workloads, and reformulates scheduling as a mathematically structured optimization problem, enabling more complete and efficient exploration of the scheduling space. Evaluations show that Crane reduces energy-delay product by up to 21.01 × and improves scheduling speed by at least 2.82 × over state-of-the-art baselines.