The process of handling the complexity that follows when optimizing inferences can be greatly minimized using TensorFlow. Model quantization is primarily used to reduce the representation of weights and also for the storage and computation of the activation function.
Using model quantization provides users with two main advantages:
Support for a variety of CPU platforms
SIMD instruction handling capabilities