efficient inference

Quantization in neural networks is the process of reducing the precision of the numbers used to represent model parameters and activations, typically to improve computational efficiency and reduce memory usage. It enables deployment of neural networks on resource-constrained devices by approximating floating-point values with lower-bit representations, often with minimal impact on accuracy.