ROSETTA: A Resource and Energy-Efficient Inference Processor for Recurrent Neural Networks Based on Programmable Data Formats and Fine Activation Pruning.
J. Kim, und T. Kim. IEEE Trans. Emerg. Top. Comput., 11 (3):
650-663(July 2023)
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%0 Journal Article
%1 journals/tetc/KimK23
%A Kim, Jiho
%A Kim, Tae-Hwan
%D 2023
%J IEEE Trans. Emerg. Top. Comput.
%K dblp
%N 3
%P 650-663
%T ROSETTA: A Resource and Energy-Efficient Inference Processor for Recurrent Neural Networks Based on Programmable Data Formats and Fine Activation Pruning.
%U http://dblp.uni-trier.de/db/journals/tetc/tetc11.html#KimK23
%V 11
@article{journals/tetc/KimK23,
added-at = {2023-09-24T00:00:00.000+0200},
author = {Kim, Jiho and Kim, Tae-Hwan},
biburl = {https://www.bibsonomy.org/bibtex/27d937ac0c83a49f243664e7200c28262/dblp},
ee = {https://doi.org/10.1109/TETC.2022.3230961},
interhash = {600d5a46e264f1c3d85d8c30a8f89214},
intrahash = {7d937ac0c83a49f243664e7200c28262},
journal = {IEEE Trans. Emerg. Top. Comput.},
keywords = {dblp},
month = {July - September},
number = 3,
pages = {650-663},
timestamp = {2024-04-08T15:30:31.000+0200},
title = {ROSETTA: A Resource and Energy-Efficient Inference Processor for Recurrent Neural Networks Based on Programmable Data Formats and Fine Activation Pruning.},
url = {http://dblp.uni-trier.de/db/journals/tetc/tetc11.html#KimK23},
volume = 11,
year = 2023
}
