EDEN: Enabling energy-efficient, high-performance deep neural network inference using approximate DRAM. Koppula, S., Orosa, L., Yağlıkçı, A G., Azizi, R., Shahroodi, T., Kanellopoulos, K., & Mutlu, O. In Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pages 166–181, 2019.
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Paper bibtex
Paper bibtex @inproceedings{koppula2019eden,
title={EDEN: Enabling energy-efficient, high-performance deep neural network inference using approximate DRAM},
author={Koppula, Skanda and Orosa, Lois and Ya{\u{g}}l{\i}k{\c{c}}{\i}, A Giray and Azizi, Roknoddin and Shahroodi, Taha and Kanellopoulos, Konstantinos and Mutlu, Onur},
booktitle={Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)},
pages={166--181},
year={2019},
url_Paper = {https://loisorosa.github.io/files/pdf/eden.pdf}
}Downloads: 0
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