Quantization

Quantization is a technique in computing and signal processing that reduces the precision of numerical values, typically by converting high-precision data (like 32-bit floating-point numbers) to lower-precision formats (like 8-bit integers). It is widely used in machine learning to compress models, reduce memory usage, and accelerate inference on hardware with limited resources. In digital signal processing, quantization converts continuous analog signals into discrete digital representations.

Also known as: Model Quantization, Post-Training Quantization, Quantization-Aware Training, QAT, PTQ

Why learn Quantization?

Developers should learn quantization primarily for deploying machine learning models efficiently on edge devices, mobile applications, or embedded systems where computational resources are constrained. It enables faster inference times and lower power consumption by reducing model size and memory bandwidth requirements. In audio and image processing, quantization is essential for analog-to-digital conversion and data compression in formats like JPEG or MP3.

Learning Resources

• docs: TensorFlow Model Optimization: Quantization
• docs: PyTorch Quantization
• tutorial: A Gentle Introduction to Quantization for Deep Learning
• tutorial: Quantization in Digital Signal Processing
• course: Efficient Deep Learning: Model Compression and Acceleration