Accelerating Neural Networks: The Power of Quantization

Quantization in machine learning reduces neural network size and computational needs by converting floating-point numbers to lower-precision integers. This technique enables efficient model deployment on embedded devices and edge hardware. The process involves mapping weights and activations to discrete levels, significantly reducing model size and speeding up inference while maintaining accuracy.