Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a 8x8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy).

翻译：低分辨率红外感应器与机器学习(ML)可被用来在室内空间实施保护隐私的社会远程监测解决方案。然而,在物(IoT)边缘节点的互联网上实施这些应用的必要性使得能源消耗变得至关重要。 在这项工作中,我们提出了一个节能的适应性推论解决方案,其中包括一个简单的觉醒触发器和8位位位数的神经神经神经网络(CNN)级联,仅用于难以分类的框架。在IoT微控制器上安装这样的适应系统,我们表明,在处理8x8低分辨率IR传感器的产出时,我们能够将静态CNN的能量消耗减少37-57%,精度下降不到2%(83%平衡精度)。