Research Article
Distributed Neural Networks for Internet of Things: The Big-Little Approach
@INPROCEEDINGS{10.1007/978-3-319-47075-7_52, author={Elias Coninck and Tim Verbelen and Bert Vankeirsbilck and Steven Bohez and Pieter Simoens and Piet Demeester and Bart Dhoedt}, title={Distributed Neural Networks for Internet of Things: The Big-Little Approach}, proceedings={Internet of Things. IoT Infrastructures. Second International Summit, IoT 360° 2015, Rome, Italy, October 27-29, 2015, Revised Selected Papers, Part II}, proceedings_a={IOT360}, year={2017}, month={6}, keywords={Deep neural networks Distributed intelligence Internet of things}, doi={10.1007/978-3-319-47075-7_52} }- Elias Coninck
Tim Verbelen
Bert Vankeirsbilck
Steven Bohez
Pieter Simoens
Piet Demeester
Bart Dhoedt
Year: 2017
Distributed Neural Networks for Internet of Things: The Big-Little Approach
IOT360
Springer
DOI: 10.1007/978-3-319-47075-7_52
Abstract
Nowadays deep neural networks are widely used to accurately classify input data. An interesting application area is the Internet of Things (IoT), where a massive amount of sensor data has to be classified. The processing power of the cloud is attractive, however the variable latency imposes a major drawback in situations where near real-time classification is required. In order to exploit the apparent trade-off between utilizing the stable but limited embedded computing power of IoT devices and the seemingly unlimited computing power of Cloud computing at the cost of higher and variable latency, we propose a Big-Little architecture for deep neural networks. A small neural network trained to a subset of prioritized output classes is running on the embedded device, while a more specific classification is calculated when required by a large neural network in the cloud. We show the applicability of this concept in the IoT domain by evaluating our approach for state of the art neural network classification problems on popular embedded devices such as the Raspberry Pi and Intel Edison.
