Deep Neural Network Discipline and Consequence to be Achieved through Internet Implementation Dropdown

Abstract

The difficulty of automatically modifying and updating operations within Deep Learning (DL) frameworks can slow down the performance of Deep Neural Network processing (DNNs). This research presents a novel approach to software optimization by leveraging dynamically collected profile data. A unique online auto-tuning system for DNNs was developed to enhance both the training and inference phases. Python Distributed Training of Neural Networks (PyDTNN) is a lightweight toolkit designed for distributed DNN training and estimation. It is utilized to evaluate the VGG19 model on two distinct multi-core architecture options. In testing, our auto-tuning system performs comparably, if not better, than a static selection strategy. The performance of each variation of PyDTNN that employs static selection remains consistently high throughout execution. Conversely, the auto-tuned version initially performs at a set level and progressively improves as more feasible choices become available. While both variations yield similar results in training, the selection strategy outperforms all other inference options by autonomously determining the best strategy for each layer in VGG19. The new online implementation selection tool assists in choosing the best performance option from numerous alternatives while the program is running. Its key features include constructing layered judgments and thoroughly examining 35 possibilities. Our advanced systems represent the optimal choice for monitoring sustainable environmental systems with maximum effectiveness, efficiency, and timeliness.