… ABSTRACT | Successful integration of deep neural networks (DNNs) or deep learning (DL)
has resulted in breakthroughs in many areas. However, deploying these highly accurate …

… network traffic on the Internet has begun outpacing server capacity to manage incoming data.
In recent years, the network … The future networks also seem to take a new direction toward …

… already support the NFV architecture natively, making the development of the Core Network
more adaptable. As cloud service providers demonstrate an increasing ability to deliver …

… for a neural network hardware accelerator. After being hyped … eg, NGVC, H265, adaptable
transformations are expected to … networks could provide an efficient method for learning these …

… -adopted deep neural network and dataset. Results show that ARES accelerates model
training on … Learning (FL) and classic SL, without sacrificing model convergence and accuracy. …

… Firstly, the adaptable prediction model will be updated based on a trained neural network to
… In this project, parallel computing programming and GPU acceleration for trivial algorithms …

… ral networks/deep learning, using deep neural networks in … adaptable unsupervised and
semisupervised deep-learning … of the code that need acceleration. Communication between …

… acceleration framework, we compare our latency prediction models based on neural networks
… To further verify the adaptability of our approach, we conduct experiments with different …

… (RL) improves the adaptability to inexperienced troubles in the implementations. The …
robustness and adaptability of Genetic Network Programming with Reinforcement Learning (GNP-…

… Machines (SVM), and neural network models to predict students' adaptability to online …
edge AI technology, thereby accelerating the intelligent transformation of various industries. …