Accelerating Neural Networks: The Power of Quantization | HackerNoon

"Neural networks are becoming larger and more complex, but their applications increasingly require running on resource-constrained devices such as smartphones, wearables, microcontrollers, and edge devices. Quantization enables: - Reducing model size: For example, switching from float32 to int8 can shrink a model by up to 4 times. - Faster inference: Integer arithmetic is faster and more energy-efficient. - Lower memory and bandwidth requirements: This is critical for edge/IoT devices and embedded scenarios."

"Quantization of Neural Networks is the process of converting the weights and activations of a neural network from high-precision formats (typically 32-bit floating-point numbers, or float32) to lower-precision formats (such as 8-bit integers, or int8). The main idea behind quantization is to 'compress' the range of possible values in order to reduce data size and speed up computations."

Quantization is a technique that reduces the size and improves the efficiency of neural networks by converting floating-point weights and activations into lower-precision integers. This article explains the quantization process in detail, illustrating how it can shrink model size, accelerate inference, and lower memory requirements—making it suitable for resource-constrained devices like smartphones and embedded systems. It also covers the implementation of quantization and dequantization in PyTorch, demonstrating its practical applications.

Read at Hackernoon