How does the choice of quantization bit-width (e.g., 8-bit, 16-bit) impact the efficiency and accuracy of a model
The choice of quantization bit-width (e.g., 8-bit, 16-bit) plays a critical role in balancing the efficiency and accuracy of machine learning models, especially deep neural networks. Quantization refers to the process of reducing the precision of the numbers used to represent model parameters and activations, quartize which can significantly reduce the computational cost and memory footprint....
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