On Different Criteria for Optimizing the Two-bit Uniform Quantizer

In this paper, we address the problem of determining the most influential parameter of the two-bit uniform quantizer, i.e. the support region threshold (
$x$
max
), according to different optimization criteria. We analyze the dependences of the quantized neural network (QNN) model quality indicators on
$\boldsymbol{x_{\max}}$
, for the case of applying two-bit uniform quantization of weights in the post-training phase for the MNIST dataset. In addition to the theoretical quality indicator of quantized signal for the Laplacian distribution - theoretical SQNR, our quality indicators are also the accuracy of QNN and the experimentally determined SQNR for the Laplacian-like weight distribution in the three-layer fully connected neural network. The goal is to determine the results of optimizing these quality indicators per X
max
for the considered research framework and to make their comparison for setting a good foundation for future research.