Modeling and compiling tensor processing applications using multi-layer adaptive data flow (ML-ADF) graphs, including folding the ML-ADF graph for temporal sharing of platform resources, computing schedules for runtime orchestration of kernel execution, memory reuse, tensor and sub-volume movement, and dataflow synchronization, and generating binary code for processors of the target computing platform and re-targetable controller code. The ML-ADF graph may represent: tensor processing of a layer of a neural network as data flow through the data nodes and distribution to compute tiles across memory hierarchy; data flow amongst layers of the neural network using connections amongst data nodes of the respective layers; and multi-dimension data partitioning and distribution using tiling parameters associated with ports of the data nodes.