TCL: Time-Dependent Clustering Loss for Optimizing Post-Training Feature Map Quantization for Partitioned DNNs

This paper introduces an enhanced approach for deploying deep learning models on resource-constrained IoT devices by combining model partitioning, autoencoder-based compression, quantization with Time Dependent Clustering Loss (TCL) regularization, and lossless compression, to reduce communication o...

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Veröffentlicht in:	IEEE access Jg. 13; S. 103640 - 103648
Hauptverfasser:	Berg, Oscar Artur Bernd, Saqib, Eiraj, Jantsch, Axel, Shallari, Irida, Krug, Silvia, Sanchez Leal, Isaac, O'Nils, Mattias
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adaptation models Autoencoders Clustering CNN Computational modeling Constraints Feature maps Internet of Things IoT Load modeling Machine learning Object detection Partitioning Quantization Quantization (signal) Real time Regularization Servers Time dependence Training
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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