Efficient Data Utilization in Deep Neural Networks for Inference Reliability

In today’s data-driven world, the exponential growth of data across various sectors presents unique opportunities and challenges. In this paper, we propose a novel method tailored to enhance the efficiency of Deep Neural Networks (DNNs) in managing these vast data amounts. The primary challenge addressed is the ability of DNNs to provide inferences on the minimal amount of data without sacrificing their quality, a significant concern given the vast scales involved in big data analytics. Our approach emphasizes DNN inference efficiency and reliability, enabling DNNs to deliver accurate inferences while substantially reducing computational complexity. This study explores the increasingly attractive deployment of DNNs for complex tasks, focusing on determining the minimal amount of data necessary to ensure optimal network performance and reliable inference outputs, improving the applicability of DNNs across various big data environments.