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 Xmax for the considered research framework and to make their comparison for setting a good foundation for future research.