DeepRT: predictable deep learning inference for cyber-physical systems

DOI10.1007/S11241-018-9314-YzbMath1436.68321DBLPjournals/rts/KangC19OpenAlexW2883839680WikidataQ57449388 ScholiaQ57449388MaRDI QIDQ779430

Jaeyong Chung, Woochul Kang

Publication date: 13 July 2020

Published in: Real-Time Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11241-018-9314-y

zbMATH Keywords

embedded systemsQoSenergy efficiencyreal-time systemcyber-physical systemsdeep learningDVFS

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Performance evaluation, queueing, and scheduling in the context of computer systems (68M20)

Related Items (1)

DeepRT

Uses Software

• Caffe
• ImageNet
• GitHub
• TensorRT
• AlexNet
• Model Zoo

Cites Work

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• Feedback control real-time scheduling: Framework, modeling, and algorithms
• Using control theory to achieve service level objectives in performance management
• Deep Learning: Methods and Applications
• Design, Implementation, and Evaluation of a QoS-Aware Real-Time Embedded Database
• Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment

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