  Intelligent entities such as self-driving vehicles, with their data being
processed by machine learning units (MLU), are developing into an intertwined
part of networks. These units handle distorted input but their sensitivity to
noisy observations varies for different input attributes. Since blind transport
of massive data burdens the system, identifying and delivering relevant
information to MLUs leads in improved system performance and efficient resource
utilization. Here, we study the integer bit allocation problem for quantizing
multiple correlated sources providing input of a MLU with a bandwidth
constraint.
  Unlike conventional distance measures between original and quantized input
attributes, a new Kullback-Leibler divergence based distortion measure is
defined to account for accuracy of MLU decisions. The proposed criterion is
applicable to many practical cases with no prior knowledge on data statistics
and independent of selected MLU instance. Here, we examine an inverted pendulum
on a cart with a neural network controller assuming scalar quantization.
Simulation results present a significant performance gain, particularly for
regions with smaller available bandwidth. Furthermore, the pattern of
successful rate allocations demonstrates higher relevancy of some features for
the MLU and the need to quantize them with higher accuracy.
