Internet of Things (IoT) sensors are nowadays heavily utilized in various real-world applications ranging from wearables to smart buildings passing by agrotechnology and health monitoring. With the huge amounts of data generated by these tiny devices, Deep Learning (DL) models have been extensively used to enhance them with intelligent processing. However, with the urge for smaller and more accurate devices, DL models became too heavy to deploy. It is thus necessary to incorporate the hardware's limited resources in the design process. Therefore, inspired by the human brain known for its efficiency and low power consumption, we propose a shallow bidirectional network based on predictive coding theory and dynamic early exiting for halting further computations when a performance threshold is surpassed. We achieve comparable accuracy to VGG-16 in image classification on CIFAR-10 with fewer parameters and less computational complexity.

翻译：物联网（IoT）传感器如今广泛应用于从可穿戴设备到智能建筑、再到农业技术和健康监测等各类实际场景。这些微型设备产生的海量数据促使深度学习（DL）模型被大量用于赋予其智能处理能力。然而，随着更小巧、更精准设备的需求日益迫切，深度学习模型因过于庞大而难以部署。因此，在设计中必须考虑硬件资源的有限性。受人类大脑高效且低功耗特性的启发，我们提出一种基于预测编码理论和动态早退机制的浅层双向网络，当性能阈值被超越时即停止进一步计算。在CIFAR-10图像分类任务中，该方法以更少的参数和更低的计算复杂度达到了与VGG-16相当的准确率。