Deep Learning Systems
Quantization is the process of mapping a large set of input values to a smaller set, typically used to reduce the precision of numerical values in deep learning models. This reduction helps to decrease the model size and improve computational efficiency, which is especially important for deploying models on resource-constrained devices. By simplifying the representation of weights and activations, quantization can lead to faster inference times and lower power consumption without significantly affecting model accuracy.
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