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The fast-growing tensor applications expose tremendous dataflow alternatives when implemented on spatial architectures that feature large PE arrays and abundant interconnection resources. Prior works develop various notations and performance models for dataflows. Though these notations are very useful for understanding the reuse, bandwidth, and performance of dataflows, they do not define the underlying hardware implementation. Due to the semantic gap, analysis based on these notations cannot capture the detailed architectural features between different dataflows, leading to inefficient design space exploration and suboptimal designs. To address these issues, we propose Rubick, a unified infrastructure for analyzing, exploring, and implementing spatial architectures. The main innovation of Rubick is it decomposes the dataflow into two low-level intermediate representations: 1) access entry and 2) data layout. Access entry specifies how data enter into the PE arrays from memory, while data layout specifies how data are arranged and accessed. These two representations allow us to infer the hardware implementation details, such as PE interconnection and memory structure, which are amenable for structural analysis and systematic exploration. Based on this decomposition analysis, Rubick provides opportunities for micro-architecture optimization and efficient design space exploration. Our experiments demonstrate that Rubick can reduce 82.4&#x0025; of wire resources with only a 2.7&#x0025; latency increase by optimizing access entry IR, and achieve 70.8&#x0025; memory overhead reduction by optimizing data layout IR. Rubick also accelerates the DSE time of dataflows by up to    $1.1\times 10^{5}\text{X}$   , saving the time from several days to minutes. The source code of Rubick is publically available on ( https://link-omitted-for-blind-review ).

Rubick: A Unified Infrastructure for Analyzing, Exploring, and Implementing Spatial Architectures via Dataflow Decomposition