However, deep learning inference and training require substantial computation resources to run quickly. Edge computing, where a fine mesh of compute nodes are placed close to end devices, is a viable way to meet the high computation and low-latency requirements of deep learning on edge devices ...
Deep learning is a promising way to get relevant information from IoT service sensor data embedded in complex situations. Due to its multifaceted structure, deep learning is better suited to the nature of computer drag. So, in the course of this article, we start by introducing deep IoT ...
VII Deep Learning Training at Edge VII-A Distributed Training at Edge VII-B Vanilla Federated Learning at Edge VII-C redCommunication-efficient FL VII-D Resource-optimized FL VII-E redSecurity-enhanced FL VIII Deep Learning for Optimizing Edge VIII-A redDL for Adaptive Edge Caching VIII-A1 Use...
各位老师打扰了,由电子科大万少华老师和河海大学巫义锐老师共同组织的专刊“Deep Learning and Edge Computing for Internet of Things”已经在Applied Sciences-Basel(中科院3区,SCI,IF:2.838)上线。专刊关注人工智能与边缘计算结合后的理论,技术与应用研究。欢迎各位老师同学不吝赐稿。论文截止日期:2023年2月20日。专刊网...
But this DL requires large datasets as well as powerful computing resources. A shortage of reliable datasets of a running pandemic is a common phenomenon. So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(...
In the context of edge computing (EC) paradigm the new type of specific System on a Chip (SoC) devices with tensor processing architectures (TPAs) appeared for running deep learning (DL) models efficiently on edge computing accelerators (ECAs). Despite availability of numerous benchmarks of ECA...
This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm (“DPSO-GA”) for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction ...
edge computing often suffers from unbalance resource allocation, which leads to task failure and affects system performance. To tackle this problem, we proposed a deep reinforcement learning(DRL)-based workload scheduling approach with the goal of balancing the workload, reducing the service time and...
Tan, T., & Cao, G. (2022, May). Deep Learning on Mobile Devices Through Neural Processing Units and Edge Computing. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications (pp. 1209-1218). IEEE. Q1 该paper解决了什么问题?
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