Optimizing Binary Neural Network for Resource-Constrained Edge Devices in IoT Applications
Published online: 01/10/2025
Corressponding author's email:
huanvm@hcmute.edu.vnDOI:
https://doi.org/10.54644/jte.2025.1756Keywords:
Binary Neural Network, XNOR-popcout, Edge device, IoT, Resource-constrained hardwareAbstract
The implementation of artificial intelligence models on edge devices is increasingly popular, bringing many values in reducing latency, effectively utilizing bandwidth, improving data security, enhancing privacy and reducing costs for users. However, this work poses many challenges in terms of accuracy, processing speed, hardware resources and model size for devices constrained by limited hardware. Binary Neural Network (BNN) is proposed as a potential solution to reduce resource requirements by using only 1 bit for quantizing. In this study, BNN network is optimized by binary quantizing both weights and activation functions with XNOR-popcout multiplication to optimize BNN network. The results show that BNN network model is lighter in memory footprint when deployed on hardware with limited computational resources, less computational time than conventional BNN network which helps the model execute faster as the network architecture becomes less complex, with acceptable accuracy on two datasets MNIST and Fashion MNIST. The proposed BNN model resul can be deployed on edge devices for IoT applications.
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