Deep learning has been popular in artificial intelligence with many applications due to great successes in many perceptual tasks (e.g., object detection, image understanding, and speech recognition). Moreover, deep learning is also critical in data science, especially for big data analytics relying on extracting high-level and complex abstractions as data representations based on a hierarchical learning process. In realizing deep learning, supervised and unsupervised approaches for training deep architectures have been empirically investigated based on the adoption of parallel computing facilities such as GPUs or CPU clusters. However, there is still limited understanding of why deep architectures work so well and how to design computationally efficient training algorithms and hardware acceleration techniques.
At the same time, the number of end devices, such as IoT (Internet of Things) devices, has dramatically increased. These devices usually aim at some deep learning-based perceptual tasks or applications as they are often directly connected to sensors (e.g., cameras) that continuously capture a large quantity of input data. However, traditional cloud-based infrastructures have been not enough for the demands of the current state of deep learning systems on end devices due to some limitations, such as associated communication costs, latency, security, and privacy concerns induced by current infrastructures. Therefore, the concepts of fog and edge computing have been recently proposed to alleviate these limitations by moving data processing capabilities closer to the network edge.
This special issue will focus on all aspects of deep learning architectures, algorithms, and applications, with particularly emphasizing exploring recent advances in perceptual applications, hardware acceleration architectures, and deep neural networks over the cloud, fog, edge, and end devices. This special issue is mainly extended from the special session on Recent Advances in Deep Learning with Applications of APSIPA 2017 conference, but any other significant contributions in the related fields are also welcome. Topics of interest include, but are not limited to:
Editor(s) of the special issue:
Dr. Li-Wei Kang (National Yunlin University of Science and Technology, Taiwan)
Dr. Wen-Huang Cheng (Academia Sinica, Taiwan)
Dr. Yuichi Nakamura (NEC Corp., Japan)
Dr. Jia-Ching Wang (National Central University, Taiwan)
Tentative schedule of submission and publication:
Submission Deadline: 31 October 2018
Expected publication date: March 2019