Journal of Engineering in Industrial Research

h-index: 18     i10-index: 25

Analyzing Baseline Models for Optimizing Deep Neural Networks in Resource-Constrained Environments

Document Type : Original Research Article

Authors

Raafi Careem 1
Gapar Md Johar 2
Ali Khatibi 2

1 Department of Computer Science and Informatics, Uva Wellassa University, Badulla, Sri Lanka

2 Management and Science University, Shah Alam, Malaysia

https://doi.org/10.48309/jeires.2024.2.2
Abstract
This study aims to identify a baseline model for optimizing deep neural network (DNN) models for deployment in resource-constrained environments (RCE). Although DNNs are excellent in many applications, their deployment on devices like wearables and mobile phones presents significant challenges. The study investigates six popular DNN models, including MobileNet (V1 and V2), ResNet50, InceptionV3, DenseNet121, and EfficientNetB1. To assess each model's advantages, disadvantages, usability, and effectiveness in RCE scenarios, a comprehensive review and empirical analysis were conducted. The analysis focuses on optimizing these models to function effectively given the limited computational power and memory of RCE devices. Key factors such as model size, computational complexity, and inference speed are examined to uncover performance trade-offs between accuracy and resource efficiency. The findings suggest that MobileNetV1 should serve as baseline models for building efficiency-focused DNN models for image classification on RCE devices. This recommendation is based on MobileNetV1's balance between performance and efficiency, making it an ideal starting point for further optimization.

Keywords

Baseline model
Deep neural network
Image classification
Optimization
Resource-constrained environments

Subjects

OPEN ACCESS

©2026 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

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Journal of Engineering in Industrial Research
Volume 5, Issue 2
Spring 2024
Pages 81-89

  • Receive Date 04 June 2024
  • Revise Date 20 October 2024
  • Accept Date 03 November 2024

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