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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Vydané v:	IEEE access Ročník 13; s. 103640 - 103648
Hlavní autori:	Berg, Oscar Artur Bernd, Saqib, Eiraj, Jantsch, Axel, Shallari, Irida, Krug, Silvia, Sanchez Leal, Isaac, O'Nils, Mattias
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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