On a real 5G testbed, offloading a CNN with early exits and splits to a MEC server reduces inference delay by up to 2.5x and energy by up to 2.6x versus local processing, but the analytical models are fitted without independent validation.
Bakhtiarnia, et al., ”Dynamic Split Computing for Efficient Deep EDGE Intelligence,” IEEE ICASSP, pp
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.NI 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Real-World Modeling of Computation Offloading for Neural Networks with Early Exits and Splits
On a real 5G testbed, offloading a CNN with early exits and splits to a MEC server reduces inference delay by up to 2.5x and energy by up to 2.6x versus local processing, but the analytical models are fitted without independent validation.