MACRO 2020, MAESTRO - A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings

This article presented a methodology to enable fast cost-benefit estimation of a DNN accelerator on a given DNN model and mapping, which consists of a compiler-friendly data-centric representation of mappings and an analytical cost-benefit estimation framework that exploits the explicit data reuse in data space in data-centric representations.

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Jiajun Wu
Jiajun Wu
PhD Student

My research interests include Hardware accelerator, reconfigurable computing and computer architecture.