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December 11, 2025ElectronicsOpen Access

Edge AI in Practice: A Survey and Deployment Framework for Neural Networks on Embedded Systems

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Authors

RCRuth Cordova-CardenasDADaniel AmorÁGÁlvaro Gutiérrez

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Overview

Survey reveals key optimization techniques for deep learning in embedded systems, suggesting improved Edge AI implementations.

Key Points

  • This work aims to establish a practical framework for deploying deep learning on embedded systems for Edge AI applications.
  • Systematic review following PRISMA principles
  • Analysis of optimization techniques like pruning and quantization
  • Examination of lightweight architectures such as CNNs and RNNs
  • Development of a five-stage methodology for deployment
  • Identified key optimization techniques beneficial for resource-constrained environments
  • Outlined emerging trends such as TinyML and hybrid architectures
  • Highlighted gaps like limited ultra-low-precision inference support and variability in hardware toolchains

Cite This Study

Cordova-Cardenas et al. (2025) studied this question.

synapsesocial.com/papers/694019342d562116f28f6f1bhttps://doi.org/10.3390/electronics14244877
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