Latency-constrained DNN architecture learning for edge systems using zerorized batch normalization

Latency-constrained DNN architecture learning for edge systems using zerorized batch normalization

Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during the design of a deep neural network to maximize performance is not intuitive. Particularly, many application scenarios are...

Ausführliche Beschreibung

Bibliographische Detailangaben
Hauptverfasser: Huai, Shuo, Liu, Di, Kong, Hao, Liu, Weichen, Subramaniam, Ravi, Makaya, Christian, Lin, Qian
Weitere Verfasser: School of Computer Science and Engineering
Format: Journal Article
Sprache:English
Veröffentlicht: 2023
Schlagworte:
Engineering::Computer science and engineering::Software::Software engineering
Latency Optimization
Edge Device
Latency Prediction
Online Zugang:https://hdl.handle.net/10356/165565

Online

https://hdl.handle.net/10356/165565

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