We propose the Sparse Abstract Machine (SAM), an intermediate representation and abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM defines a streaming abstraction with sparse primitives that encompass a large space of scheduled tensor algebra expressions. SAM dataflow graphs naturally separate tensor formats from algorithms and is expressive enough to incorporate many sparse-iteration and hardware-specific optimizations. We show an automatic compilation technique from a high-level language to SAM and a set of hardware primitives which implement it. We evaluate the generality and extensibility of our sparse abstract machine, explore the performance space of sparse tensor algebra optimizations using SAM, and provide an example implementation of our SAM architecture.
翻译:我们建议采用Sprassy摘要机(SAM),这是一个中间代表器和抽象机器模型,用于针对稀疏的高温代数,以重新配置和固定功能的空间数据流加速器。SAM定义了与稀散原始体的流式抽象体,其中包括大量预定的高温代数表达式空间。SAM数据流图自然地将Exorm格式与算法分离,并足以包含许多稀释和硬件优化。我们展示了一种从高语言到SAM的自动汇编技术,以及一套用于实施这一技术的硬件原始体。我们评估了我们稀散的抽象体的普通性和可扩展性,探索了使用SAM的稀散高温代数优化的性能空间,并举例介绍了我们SAM结构的落实情况。