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1 Ergebnisse
1
Darkside: 2.6GFLOPS, 8.7mW Heterogeneous RISC-V Cluster for..:
, In:
ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC)
,
Garofalo, Angelo
;
Perotti, Matteo
;
Valente, Luca
... - p. 273-276 , 2022
Link:
https://doi.org/10.1109/ESSCIRC55480.2022.9911384
RT T1 <cTitle>ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC)</cTitle>: T1 <Title>Darkside: 2.6GFLOPS, 8.7mW Heterogeneous RISC-V Cluster for Extreme-Edge On-Chip DNN Inference and Training</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9911384&Exemplar=1&LAN=DE A1 Garofalo, Angelo A1 Perotti, Matteo A1 Valente, Luca A1 Tortorella, Yvan A1 Nadalini, Alessandro A1 Benini, Luca A1 Rossi, Davide A1 Conti, Francesco YR 2022 K1 Training K1 Deep learning K1 Tensors K1 Convolution K1 Neural networks K1 Throughput K1 System-on-chip K1 Extreme-Edge K1 Heterogeneous Cluster K1 Tensor Product Engine K1 Ultra-Low-Power AI SP 273 OP 276 LK http://dx.doi.org/https://doi.org/10.1109/ESSCIRC55480.2022.9911384 DO https://doi.org/10.1109/ESSCIRC55480.2022.9911384 SF ELIB - SuUB Bremen
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