Model Compression for Efficient Deployment: Analyzing model compression techniques to reduce the size of machine learning models for efficient deployment on resource-constrained devices

Abstract

Model compression techniques play a crucial role in deploying machine learning models on resource-constrained devices such as smartphones, IoT devices, and edge devices. These techniques aim to reduce the size of the models while maintaining their performance. This paper provides an overview of various model compression techniques, including quantization, pruning, knowledge distillation, and compact architectures. We analyze the effectiveness of these techniques in terms of model size reduction, inference speedup, and memory footprint reduction. We also discuss the trade-offs between model size reduction and performance degradation. Additionally, we examine the challenges and future directions of model compression for efficient deployment.