  Recently, a new wave of full modeling analyses have emerged within the
Large-Scale Structure community, leading mostly to tighter constraints on the
estimation of cosmological parameters, when compared with standard approaches
used over the last decade by collaboration analyses of stage III experiments.
However, the majority of these full-shape analyses have primarily been
conducted in Fourier space, with limited emphasis on exploring the
configuration space. Investigating n-point correlations in configuration space
demands a higher computational cost compared to Fourier space because it
typically requires an additional integration step. This can pose a limitation
when using these approaches, especially when considering higher-order
statistics. One avenue to mitigate the high computation time is to take
advantage of neural network acceleration techniques. In this work, we present a
full shape analysis of Sloan Digital Sky Survey III/BOSS in configuration space
using a neural network accelerator. We show that the efficacy of the pipeline
is enhanced by a time factor $10^{3}$ without sacrificing precision, making it
possible to reduce the error associated with the surrogate modeling to below
$10^{-2}$ percent which is compatible with the precision required for current
stage IV experiments such as DESI. We find $\Omega_m=0.286\pm 0.009$,
$H_0=68.8\pm 1.2$ $\mathrm{km} \mathrm{s^{-1}}\mathrm{Mpc^{-1}}$ and $A_s
\times 10^9 =2.09 ^{+0.25}_{-0.29}$. Our results on public BOSS data are in
good agreement with BOSS official results and compatible with other independent
full modeling analyses. We explore relaxing the prior on $\omega_b$ and varying
$n_s$, without significant changes in the mean values of the cosmological
parameters posterior distributions, but enlarging their widths. Finally, we
explore the information content of the multipoles when constraining
cosmological parameters.
