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.

中文翻译：

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比特分配策略对速率受限系统中机器学习性能的影响

诸如自动驾驶汽车之类的智能实体，其数据由机器学习单元 (MLU) 处理，正在发展成为网络中相互交织的部分。这些单元处理失真的输入，但它们对噪声观察的敏感性因不同的输入属性而异。由于海量数据的盲目传输会给系统带来负担，因此识别相关信息并将其传递给 MLU 可以提高系统性能和有效利用资源。在这里，我们研究了整数位分配问题，用于量化多个相关源，提供具有带宽约束的 MLU 输入。与原始和量化输入属性之间的传统距离度量不同，定义了一种新的基于 Kullback-Leibler 散度的失真度量来考虑 MLU 决策的准确性。所提出的标准适用于许多没有数据统计先验知识且独立于所选 MLU 实例的实际案例。在这里，我们使用假设标量量化的神经网络控制器检查推车上的倒立摆。仿真结果带来了显着的性能提升，特别是对于可用带宽较小的区域。此外，成功的速率分配模式表明 MLU 的某些特征具有更高的相关性，并且需要以更高的准确度对其进行量化。特别是对于可用带宽较小的地区。此外，成功的速率分配模式表明 MLU 的某些特征具有更高的相关性，并且需要以更高的准确度对其进行量化。特别是对于可用带宽较小的地区。此外，成功的速率分配模式表明 MLU 的某些特征具有更高的相关性，并且需要以更高的准确度对其进行量化。