quantization-aware inference
Quantization-aware inference is a technique used in machine learning to perform inference or prediction using quantized neural networks. It involves optimizing the neural network model for deployment on hardware with limited precision, such as low-bit fixed-point or binary arithmetic. This approach aims to strike a balance between model accuracy and computational efficiency by ensuring that the inference process accounts for the effects of quantization during training. By incorporating quantization-aware techniques into the inference pipeline, the model can effectively leverage the advantages of reduced precision while minimizing any potential loss in performance.
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