Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications-Reference-Cited by-同舟云学术

Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications

Published:2020-06 Issue: Volume:521 Page:107-143
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Baccarelli Enzo^ORCID,Scardapane Simone^ORCID,Scarpiniti Michele^ORCID,Momenzadeh Alireza,Uncini Aurelio

Funder

Sapienza University of Rome

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference39 articles.

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3. Fog of everything: energy-efficient networked computing architectures, research challenges, and a case study;Baccarelli;IEEE Access,2017

4. EcoMobiFog – Design and dynamic optimization of a 5G Mobile-Fog-Cloud multi-tier ecosystem for the real-time distributed execution of stream applications;Baccarelli;IEEE Access,2019

5. LCNN: Lookup-based convolutional neural network;Bagherinezhad,2017

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