TL: Automatic End-to-End Compiler of Tile-Based Languages for Spatial Dataflow Architectures

Spatial dataflow accelerators are a promising direction for next-generation computer systems because they can reduce the memory bottlenecks of traditional von Neumann machines such as CPUs and GPUs. They do so by organizing computation around explicit, compiler-managed data movement over the on-chip network, allowing operands to be directly forwarded between processing elements and reducing reliance on high-latency, bandwidth-limited global shared memory. Such localized communications can provide higher throughput and efficiency compared to repeated off-chip memory accesses. However, their end-to-end performance depends strongly on how workloads are mapped to the hardware. Naive mappings can perform very poorly, and most users rely on hand-tuned vendor libraries. In practice, although existing spatial-dataflow accelerators have strong potential for high performance, energy- and cost-efficiency, their limited programmability remains a major barrier to their wider adoption. This paper presents TL, an end-to-end framework that compiles tile-based programs (such as Triton kernels) onto spatial dataflow architectures. Unlike most existing compiler frameworks that focus on optimizing code generation within a single tile, TL addresses the central challenge of distributing tile instances across spatially distributed cores and exploiting the on-chip network and distributed memories to increase data reuse and reduce communications. TL proposes a hardware representation that captures interconnect topology, memory hierarchy, and compute capabilities, enabling both specialized architecture-specific optimizations and support for diverse spatial dataflow targets. TL is built on the MLIR ecosystem and defines a generic entry point for different front-ends and an end point for different back-ends.