DeepRT: predictable deep learning inference for cyber-physical systems
type: Item
instance of: scholarly article
title: DeepRT: predictable deep learning inference for cyber-physical systems
zbMATH Open document ID: 1436.68321
author: Woochul Kang
author: Jaeyong Chung
published in: Real-Time Systems
publication date: 2020-07-13T00:00:00Z
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 68M20
zbMATH DE Number: 7219879
zbMATH Keywords: deep learning
zbMATH Keywords: QoS
zbMATH Keywords: real-time system
zbMATH Keywords: embedded systems
zbMATH Keywords: energy efficiency
zbMATH Keywords: cyber-physical systems
zbMATH Keywords: DVFS
Wikidata QID: Q57449388
describes a project that uses: Model Zoo
describes a project that uses: AlexNet
describes a project that uses: Caffe
describes a project that uses: ImageNet
describes a project that uses: TensorRT
describes a project that uses: GitHub
MaRDI profile type: Publication
full work available at URL: s11241-018-9314-y
OpenAlex ID: W2883839680
cites work: Deep Learning: Methods and Applications
cites work: Q5270493
cites work: Design, Implementation, and Evaluation of a QoS-Aware Real-Time Embedded Database
cites work: Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
cites work: Feedback control real-time scheduling: Framework, modeling, and algorithms
cites work: Using control theory to achieve service level objectives in performance management
cites work: Q5741609
cites work: Q4252722
DBLP publication ID: journals/rts/KangC19
DOI: 10.1007/S11241-018-9314-Y
description: scientific article; zbMATH DE number 7219879