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.
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.
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