# Quantization (Edge AI Glossary)

## metadata
- term: Quantization
- slug: quantization
- letter: Q
- route: /resources/glossary/quantization
- canonical: https://klyff.com/resources/glossary/quantization

## Definition & Context
Quantization is the process of reducing the precision of a neural network’s weights and activations, converting them from high-bit formats (e.g., 32-bit floating point) to...

## Details
## Quantization

#### What is Quantization?

Quantization is the process of reducing the precision of a neural network’s weights and activations, converting them from high-bit formats (e.g., 32-bit floating point) to lower-bit representations (e.g., 8-bit integers). Also called **model compression**, it enables faster and more energy-efficient AI inference on edge devices without significant loss of accuracy.

#### Why Is It Used?

Quantization is used to optimize AI models for resource-constrained devices, such as IoT sensors, edge servers, and embedded systems. It reduces memory usage, computation costs, and power consumption, making real-time AI feasible outside cloud environments.

#### How Is It Used?

 
- During model training (quantization-aware training) to maintain accuracy.
- Post-training quantization to compress pre-trained models.
- Integrated into Edge AI pipelines for devices like cameras, drones, and smart sensors. 

#### Types of Quantization

 
- **Post-Training Quantization (PTQ):** Converts trained models to lower precision.
- **Quantization-Aware Training (QAT):** Incorporates quantization during training to preserve model performance.
- **Dynamic Quantization:** Adjusts precision during runtime for specific layers or operations. 

#### Benefits of Quantization

 
- **Reduced Model Size:** Lowers storage and memory requirements.
- **Faster Inference:** Speeds up AI computations on edge devices.
- **Lower Power Consumption:** Critical for battery-powered IoT and edge devices.
- **Edge Compatibility:** Enables deployment of complex AI models on constrained hardware.