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1 Ergebnisse
1
DLFloat: A 16-b Floating Point Format Designed for Deep Lea..:
, In:
2019 IEEE 26th Symposium on Computer Arithmetic (ARITH)
,
Agrawal, Ankur
;
Mueller, Silvia M.
;
Fleischer, Bruce M.
... - p. 92-95 , 2019
Link:
https://doi.org/10.1109/ARITH.2019.00023
RT T1
2019 IEEE 26th Symposium on Computer Arithmetic (ARITH)
: T1
DLFloat: A 16-b Floating Point Format Designed for Deep Learning Training and Inference
UL https://suche.suub.uni-bremen.de/peid=ieee-8877411&Exemplar=1&LAN=DE A1 Agrawal, Ankur A1 Mueller, Silvia M. A1 Fleischer, Bruce M. A1 Sun, Xiao A1 Wang, Naigang A1 Choi, Jungwook A1 Gopalakrishnan, Kailash YR 2019 SN 2576-2265 K1 Training K1 Deep learning K1 Hardware K1 Dynamic range K1 Adders K1 Engines K1 Image recognition K1 reduced precision computation, floating point, machine learning, deep learning SP 92 OP 95 LK http://dx.doi.org/https://doi.org/10.1109/ARITH.2019.00023 DO https://doi.org/10.1109/ARITH.2019.00023 SF ELIB - SuUB Bremen
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