NNCF quantization

What is NNCF quantization ?

NNCF (Neural Network Compression Framework) quantization is a technique used to reduce the size of deep learning models and improve inference speed by converting model weights and activations from floating-point to lower-bit integer representations. This process maintains model accuracy while enabling faster and more efficient deployment on resource-constrained devices.

Features

• Flexible quantization methods: Supports various quantization techniques, including post-training quantization and quantization-aware training.
• Cross-platform compatibility: Works seamlessly with popular frameworks like TensorFlow, PyTorch, and ONNX.
• Automatic quantization: Simplifies the process with automated configuration for optimal performance.
• Accuracy preservation: Built-in mechanisms to recover accuracy post-quantization.
• Hardware-aware optimization: Tailors quantization for specific hardware accelerators.
• Extensive support:Compatible with multiple model architectures and quantization algorithms.

How to use NNCF quantization ?

1. Install NNCF: Use pip to install the package: pip install nncf.
2. Import the library: Add from nncf import NNCFConfig to your code.
3. Load your model: Prepare your pre-trained model (e.g., MobileNet).
4. Configure quantization: Define quantization settings using NNCFConfig.
5. Apply quantization: Use the configuration to create a quantized model.
6. Export the model: Convert the quantized model to the desired format (e.g., OpenVINO IR).

Frequently Asked Questions

What models are supported by NNCF quantization?
NNCF supports a wide range of models, including popular architectures like MobileNet, ResNet, and Inception. It is framework-agnostic and works with TensorFlow, PyTorch, and ONNX models.

Is NNCF quantization free to use?
Yes, NNCF is open-source and free to use under the Apache 2.0 license. It is actively maintained by Intel and the OpenVINO community.

How does NNCF ensure accuracy after quantization?
NNCF employs quantization-aware training and automatic accuracy recovery techniques to minimize accuracy loss. These methods fine-tune the model during quantization to maintain performance.