Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.

The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...

Quantization is a widely adopted technique in model deployment as it offers a favorable trade-off between computational overhead and performance loss. Integer-arithmetic-only quantization is an ...