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Creators/Authors contains: "Won, Jaeyeon"

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  1. ; ; ; ; ; ; ; (, Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems)
    We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM defines a streaming dataflow abstraction with sparse primitives that encompass a large space of scheduled tensor algebra expressions. SAM dataflow graphs naturally separate tensor formats from algorithms and are expressive enough to incorporate arbitrary iteration orderings and many hardware-specific op timizations. We also present Custard, a compiler from a high-level language to SAM that demonstrates SAM's usefulness as an intermediate representation. We automatica lly bind from SAM to a streaming dataflow simulator. We evaluate the generality and extensibility of SAM, explore the performance space of sparse tensor algebra optim izations using SAM, and show SAM's ability to represent dataflow hardware. 
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