- Abstract—We propose distributed deep neural networks
- (DDNNs) over distributed computing hierarchies, consisting of
- the cloud, the edge (fog) and end devices. While being able to
- accommodate inference of a deep neural network (DNN) in the
- cloud, a DDNN also allows fast and localized inference using
- shallow portions of the neural network at the edge and end
- devices. When supported by a scalable distributed computing
- hierarchy, a DDNN can scale up in neural network size and
- scale out in geographical span. Due to its distributed nature,
- DDNNs enhance sensor fusion, system fault tolerance and data
- privacy for DNN applications. In implementing a DDNN, we
- map sections of a DNN onto a distributed computing hierarchy.
- By jointly training these sections, we minimize communication
- and resource usage for devices and maximize usefulness of
- extracted features which are utilized in the cloud. The resulting
- system has built-in support for automatic sensor fusion and
- fault tolerance. As a proof of concept, we show a DDNN
- can exploit geographical diversity of sensors to improve object
- recognition accuracy and reduce communication cost. In our
- experiment, compared with the traditional method of offloading
- raw sensor data to be processed in the cloud, DDNN locally
- processes most sensor data on end devices while achieving high
- accuracy and is able to reduce the communication cost by a
- factor of over 20x
[hide][/hide] |
熟悉论坛请点击新手指南
京公网安备 11010802022788号
论坛法律顾问：王进律师
知识产权保护声明
免责及隐私声明
