Quantization is a powerful technique used in deep learning to reduce the memory and computational requirements of neural networks by representing weights and activations with fewer bits. In this section, we'll delve into the concept of quantization, elucidating its significance and showcasing its application through examples and diagrams. Understanding Quantization: Quantization involves approximating the floating-point parameters of a neural network with fixed-point or integer representations. By reducing the precision of these parameters, quantization enables the compression of model size and accelerates inference speed, making deep learning models more efficient and deployable on resource-constrained devices. The Process of Quantization: The quantization process typically consists of two main steps: Weight Quantization : In weight quantization, the floating-point weights of the neural network are converted into fixed-point or integer representations with reduced precision. This ...
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