Model Compression (Pruning and Quantization Strategies)

Dr. Srinivas Rana

Date : 02/04/2025

Abstract:

Model compression, focusing specifically on pruning and quantization strategies, is an essential topic in machine learning. As machine learning models become more complex and resource-demanding, exploring ways to make them more efficient without sacrificing their performance is crucial. Pruning and quantization are two key techniques that help reduce the size of models, improve inference speed, and lower resource consumption, making them more suitable for deployment on edge devices or in environments with limited computational power.