A comprehensive review of model compression techniques in machine learning
Universidade Federal do Amazonas · University of Manchester
Abstract
Abstract This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things (IoT) systems. By systematically exploring compression techniques and lightweight design architectures, it is provided a comprehensive understanding of their operational contexts and effectiveness. The synthesis of these strategies reveals a dynamic interplay between model performance and computational demand, highlighting the balance required for optimal application. As machine learning (ML) models grow increasingly complex and…
Citation impact
- FWCI
- 56.81
- Percentile
- 100%
- References
- 291
Authors
4Topics & keywords
- Computer science
- Software deployment
- Artificial intelligence
- Field (mathematics)
- Resource efficiency
- Distributed computing
- Machine learning
- Software engineering