How to Choose AI Model Quantization Techniques | AI Model Optimization with Intel® Neural Compressor

Learn the fundamentals of AI model quantization. Your application and project have unique requirements, so there are a variety of quantization techniques. See an overview of each technique, tradeoffs, and recommended applications.

AI model quantization is one of the most popular ways to optimize models for deployment. Reducing word lengths of weights and activations reduces the model size and can speed up inference. However, there are a variety of techniques to choose from.

Learn the first principles of what is required to quantize floating-point models to integer formats. This is followed by an overview of each of the main model quantization approaches, covering the amount of effort required and the benefits of each. Subsequent videos in this series will cover each technique and how to use them in Intel Neural Compressor.

About the AI Model Optimization with Intel® Neural Compressor Series:
Learn how to choose and get started with AI model optimization techniques. Get started with examples using Intel® Neural Compressor, which works within PyTorch*, TensorFlow*, and ONNX* Runtime

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How to Choose AI Model Quantization Techniques | AI Model Optimization with Intel® Neural Compressor

[AnthonyKostalvazque]

How can i define my own quantization methods?