Model Compression Techniques for Edge Deployment

Deploying machine learning models on edge devices, such as smartphones, IoT sensors, embedded systems, and microcontrollers requires careful optimization due to constraints in memory, compute power, latency, and energy consumption. Model compression is a critical set of techniques that reduce the size and computational requirements of models while preserving acceptable accuracy. This article explores the most effective and widely used model compression techniques, along with their underlying pri