Surveying Lightweight Neural Network Architectures for Enhanced Mobile Performance

This study explores the creation of lightweight neural network architectures specifically designed for mobile devices. The main focus is on MobileNet, but we also examine other models like ShuffleNet, SqueezeNet, EfficientNet, MnasNet, and NASNet Mobile. The main challenge is to carefully evaluate these models to find the best balance between simplicity, accuracy, and efficiency, considering the diverse needs of mobile applications. We assess important metrics such as simplicity, accuracy, and efficiency within these architectures. The main goal is to provide guidance to practitioners in choosing the most suitable architecture. We offer insights into the trade-offs and advantages of each model through both quantitative and qualitative assessments. We consider factors like computational resources, accuracy requirements, and processing speed. The findings of this research provide valuable insights for practitioners who want to make informed decisions about the best neural network architecture for mobile devices. This guidance is tailored to their specific computational limitations and application requirements, helping them make strategic decisions in this specialized field.