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1
DNN Memory Footprint Reduction via Post-Training Intra-Laye..:
, In:
2024 25th International Symposium on Quality Electronic Design (ISQED)
,
Ghavami, Behnam
;
Kamjoo, Amin
;
Shannon, Lesley
. - p. 1-7 , 2024
Link:
https://doi.org/10.1109/ISQED60706.2024.10528676
RT T1 <cTitle>2024 25th International Symposium on Quality Electronic Design (ISQED)</cTitle>: T1 <Title>DNN Memory Footprint Reduction via Post-Training Intra-Layer Multi-Precision Quantization</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10528676&Exemplar=1&LAN=DE A1 Ghavami, Behnam A1 Kamjoo, Amin A1 Shannon, Lesley A1 Wilton, Steve YR 2024 SN 1948-3295 K1 Privacy K1 Quantization (signal) K1 Computational modeling K1 Bit rate K1 Training data K1 Channel estimation K1 Computer architecture K1 Deep neural network K1 Edge devices K1 Compression K1 Quantization on DNN K1 Memory footprint SP 1 OP 7 LK http://dx.doi.org/https://doi.org/10.1109/ISQED60706.2024.10528676 DO https://doi.org/10.1109/ISQED60706.2024.10528676 SF ELIB - SuUB Bremen
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