activation quantization
Activation quantization refers to the process of reducing the precision of neural network activations during inference. It involves quantizing the continuous values of activations to a fixed number of discrete levels, typically using low-precision data types such as integers or fixed-point representations. This technique allows for more efficient computations and memory usage, enabling the deployment of neural networks on devices with limited resources, such as mobile phones or embedded systems. Activation quantization provides a trade-off between model size and accuracy, as it can introduce quantization errors that affect the overall performance of the network.
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