Nome da Revista: Neurocomputing
Classificação: B1
Dossiê Temático: Edge Intelligence: Neurocomputing Meets Edge Computing
Prazo: 30/09/2020
Titulação: Sem informação.
Recent years have witnessed the proliferation of mobile computing and Internet-of-Things (IoT), where billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the network edge. Driving by this trend and the development of 5G, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability and energy efficiency. In the meanwhile, with the development of neural networks, Artificial Intelligence (AI) has been applied to a variety of disciplines and proved highly successful in a vast class of intelligent applications cross many domains.
Recently, edge intelligence, aiming to facilitate the deployment of neural networks on edge computing, has received significant attention. However there are many challenges existing for a novel design of edge computing architecture to AI applications, and their co-optimization. For instance, conventional neural networks techniques usually entail powerful computing facilities (e.g., cloud computing platforms), while the entities at the edge may have only limited resources for computations and communications. This suggests that AI algorithms should be revisited for edge computing to AI models into the edge device for efficient processing. On the other hand, the adapted deployments of neural networks at the edge empower the efficient learning systems that can provide the “smartification” across different layers, e.g., from network communications to applications, and also involve collaborations across edge to cloud. Finally, designing algorithms for small-scale edge devices in a learning ambience is all the challenging as there are several conflicting issues to account for. These include, memory management, power management, and compute capability of a node, etc.
In this special issue, we solicit original work on ML/AI, specifically catered to deep neural networks on/for edge computing, and efficient learning systems on edge computing, addressing specific challenges in this field. The list of possible topics includes, but not limited to:
Neuromorphic computing challenges on Edge devicesIntelligent Edge Computing Devices for neurocomputing applicationsSpiking Netural Networks on Edge devices - low-power and memory bandwidth challenges Conventional Neural Networks algorithms on edge computingNeurocomputing Algorithms on edge devices for Wearables;Edge/Fog-infused Cloud architectures for ML/AI applicationsEfficient Artificial intelligence algorithms on edge computingFew-shot learning on edge devices for ML/AI applicationsResource and data management for edge intelligenceAI/ML for small-scale low-power edge devicesDistributed and cooperative learning with edge devices on CloudApplications of edge intelligence & neurocomputing5G-enabled services for edge intelligence & neurocomputingSystem architectures of edge based neurocomputingArchitecture & application of Edge AI for IoTSecurity & privacy for edge computingAttack mitigation in edge computing.
2. Submission Guidelines
Authors should prepare their manuscripts according to the "Instructions for Authors" guidelines of “Neurocomputing” outlined at the journal website https://www.elsevier.com/journals/neurocomputing. Authors need to explicitly identify an appropriate closely matching topic from the list specified above in their cover letters during submission. All papers will be peer-reviewed following a regular reviewing procedure. Each submission should clearly demonstrate evidence of benefits to society or large communities. Originality and impact on society, in combination with a media-related focus and innovative technical aspects of the proposed solutions will be the major evaluation criteria.
3. Important Dates
Submission Deadline: 30th September 2020
First Review Decision: 31st December 2020
Revisions Due: 31st January 2021
Decision on the Accepted manuscripts: 28th February 2021
Expected publication date: 30th April 2021