%0 Conference Paper %1 conf/arith/GarofaloTCBR21 %A Garofalo, Angelo %A Tagliavini, Giuseppe %A Conti, Francesco %A Benini, Luca %A Rossi, Davide %B ARITH %D 2021 %I IEEE %K dblp %P 53 %T XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V based IoT End Nodes. %U http://dblp.uni-trier.de/db/conf/arith/arith2021.html#GarofaloTCBR21 %@ 978-1-6654-2293-2

@inproceedings{conf/arith/GarofaloTCBR21, added-at = {2021-11-23T00:00:00.000+0100}, author = {Garofalo, Angelo and Tagliavini, Giuseppe and Conti, Francesco and Benini, Luca and Rossi, Davide}, biburl = {https://www.bibsonomy.org/bibtex/268845805e156d18ccd2b92849db95af5/dblp}, booktitle = {ARITH}, crossref = {conf/arith/2021}, ee = {https://doi.org/10.1109/ARITH51176.2021.00020}, interhash = {96997ce812786cf097de0f0d89111cb0}, intrahash = {68845805e156d18ccd2b92849db95af5}, isbn = {978-1-6654-2293-2}, keywords = {dblp}, pages = 53, publisher = {IEEE}, timestamp = {2024-04-09T16:06:58.000+0200}, title = {XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V based IoT End Nodes.}, url = {http://dblp.uni-trier.de/db/conf/arith/arith2021.html#GarofaloTCBR21}, year = 2021 }