Layer-Wise Filter Thresholding Based CNN Pruning for Efficient IoT Edge Implementations
作者：
Kalyanam L.K.;Joshi R.;Katkoori S.
通讯作者：
Kalyanam, L.K.
作者机构：
University of South Florida, Tampa, FL, United States
通讯机构：
University of South FloridaUnited States
语种：
英文
关键词：
Convolutional Neural Networks,IoT edge devices,Resource optimization,Threshold-based pruning
期刊：
IFIP Advances in Information and Communication Technology
ISSN：
1868-422X
年：
2024
卷：
683 AICT
页码：
76-93
会议名称：
6th IFIP International Conference on Internet of Things, IFIP IoT 2023
会议时间：
2 November 2023 through 3 November 2023
摘要：
This paper presents a novel approach for efficiently running convolutional neural networks (CNNs) on Internet of Things (IoT) edge devices. The proposed method utilizes threshold-based pruning to optimize pre-trained CNN models, enabling inference on resource-constrained IoT and edge devices. The pruning thresholds for each layer are iteratively adjusted using a range-based threshold pruning technique. The pre-trained network evaluates the accuracy of the pruned model and dynamically adjusts the pruning thresholds to maximize accuracy. The effectiveness of the proposed approach is validated on the widely-used LeNet benchmark network, with MNIST, Fashion-MNIST, and SVHN d...
