quantized neural networks
Quantized neural networks are a type of neural network architecture that utilize quantization techniques to reduce the precision of numerical values used in the network's computations. This involves representing the parameters and activations as fixed-point numbers with a limited number of bits. The quantization process enables more efficient implementation of neural networks on hardware devices, reducing memory consumption and computational requirements. It enables faster inference times, lower energy consumption, and can be particularly beneficial for deploying neural networks on resource-constrained devices such as smartphones and embedded systems.
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