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

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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Bibliographic Details
Main Authors: Oscar Artur Bernd Berg, Eiraj Saqib, Axel Jantsch, Irida Shallari, Silvia Krug, Isaac Sanchez Leal, Mattias O'Nils
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
CNN
partitioning
quantization
IoT
Online Access:https://ieeexplore.ieee.org/document/11031457/
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https://ieeexplore.ieee.org/document/11031457/

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