Optimizing FCN for devices with limited resources using quantization and sparsity enhancement.

ABSTRACT: This study addresses the optimization of fully convolutional networks (FCNs) for deployment on resource-limited devices in real-time scenarios. While prior research has extensively applied quantization techniques to architectures like VGG-16, there is limited exploration of comprehensive layer-wise quantization specifically within the FCN-8 architecture. To fill this gap, we propose an innovative approach utilizing full-layer quantization with an [Formula: see text] error minimization algorithm, accompanied by sensitivity analysis to optimize fixed-point representation of network weights. Our results demonstrate that this method significantly enhances sparsity, achieving up to 40%, while preserving performance, yielding an impressive 89.3% pixel accuracy under extreme quantization conditio

SUBMITTER: Faizan-Khan M 

PROVIDER: S-EPMC12322192 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature