Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...
Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.
No matter how many bits an analog-to-digital converter (ADC) provides, the digital output can only approximate the original signal. This approximation gives rise to ...
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