Edge AI Inference Optimization: Quantization and Pruning on Resource Constrained Platforms

Authors

  • Ishan Pardesi

Keywords:

Edge AI Optimization, Neural Network Quantization, Model Pruning, Embedded Deep Learning, Resource-Constrained Inference

Abstract

The deployment of sophisticated artificial intelligence models on resource-constrained embedded systemspresents fundamental challenges in balancing computational efficiency with accuracy preservation. Contemporary edge devices, including

References

Yiwen Guo, et al., "Dynamic Network Surgery for Efficient DNNs," arxiv, 2016. Available: https://arxiv.org/abs/1608.04493

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Published

2025-12-11

How to Cite

Ishan Pardesi. (2025). Edge AI Inference Optimization: Quantization and Pruning on Resource Constrained Platforms . Journal of Computational Analysis and Applications (JoCAAA), 34(12), 385–393. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4367

Issue

Vol. 34 No. 12 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.