The front deployed deep learning system is a promising technology for the next generation
of industrial applications, which can extract essential information from high dimension …

We propose a novel and adaptive feature space distillation method (AFSD) to reduce the
communication overhead among distributed computers. The proposed method improves the …

Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …

Recent advances in deep learning are driven by the growing scale of computation, data, and
models. However, efficiently training large-scale models on distributed systems requires an …

The distributed deep learning architecture can support the front-deployment of deep
learning systems in resource constrained Internet of Things devices and is attracting …

Many recent pattern recognition applications rely on complex distributed architectures in
which sensing and computational nodes interact together through a communication network …

To fully unleash the potential of edge devices, it is popular to cut a neural network into
multiple pieces and distribute them among available edge devices to perform inference …

최근 알파고를 통해 인공지능 기술이 전 세계인의 이목을 집중시켰던 반면, 인공지능
연구자들은 인공지능 부활에 결정적 역할을 한 딥러닝 기술에 주목하고 있다. 딥러닝은 다계층 …

This paper investigates the problem of performing distributed deep learning (DDL) to train
machine learning (ML) models at the edge with resource-constrained embedded devices …

The front deployed deep learning is a promising technology of the next generation industrial
applications, which can extract essential information from high dimension sensors. However …