Marvel: A Data-Centric Approach for Mapping Deep Learning Operators on Spatial Accelerators

A spatial accelerator’s efficiency depends heavily on both its mapper and cost models to generate optimized mappings for various operators of DNN models. However, existing cost models lack a formal boundary over their input programs (operators) for accurate and tractable cost analysis of the mapping...

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Veröffentlicht in:	ACM transactions on architecture and code optimization 2022-03, Vol.19 (1), p.1-26
Hauptverfasser:	Chatarasi, Prasanth, Kwon, Hyoukjun, Parashar, Angshuman, Pellauer, Michael, Krishna, Tushar, Sarkar, Vivek
Format:	Artikel
Sprache:	eng
Online-Zugang:	Volltext
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