Quantization
Quantization is a model compression technique in AI that reduces the numerical precision of parameters (weights and activations). For example, 32-bit floating-point values (FP32) are converted to 16-bit (FP16) or 8-bit integer (INT8) formats. This reduces model size and speeds up inference, making it particularly suitable for edge and embedded applications.
Background
Quantization belongs to the family of model optimization strategies, along with pruning and knowledge distillation. It has gained traction as deep learning models are increasingly deployed on devices with limited resources, where memory and power efficiency are crucial.
Examples
- Computer vision: running object detection models on mobile phones in real time.
- Speech and NLP: compressing large transformer models for on-device assistants.
- IoT and robotics: enabling AI on edge devices with limited hardware.
Strengths and weaknesses
- ✅ Smaller memory footprint and faster inference.
- ✅ Lower energy consumption.
- ❌ May reduce accuracy if not carefully calibrated.
- ❌ Requires hardware and software support for efficient implementation.
Quantization is not just about compressing numbers; it represents a fundamental trade-off between efficiency and accuracy. There are two major approaches: post-training quantization, which is lightweight but may degrade performance, and quantization-aware training, where the model is trained under quantization constraints, producing higher-quality results.
An important nuance is layer sensitivity. Not all parts of a network respond equally to reduced precision. Early convolutional layers or embedding layers may need higher precision, while fully connected layers or deeper stages can be quantized more aggressively. Adaptive quantization strategies therefore allow developers to balance accuracy and efficiency.
Recent research has pushed quantization even further, exploring ultra-low precision formats such as 4-bit integers (INT4) or even binary networks. While these approaches enable extremely fast and energy-efficient inference, they often struggle with maintaining acceptable accuracy in real-world use cases.
Ultimately, quantization is one of the enablers of edge AI. By making models compact and energy-efficient, it bridges the gap between powerful data center models and real-time inference on mobile devices, wearables, or autonomous systems.
📚 Further Reading
- Jacob, B. et al. (2018). Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.
- Han, S. et al. (2016). Deep Compression.
