Partitioning of CNN Models for Execution on Fog Devices

Fog Computing has in recent times captured the imagination of industrial and research organizations working on various aspects of connected livelihood and governance of smart cities. Improvements in deep neural networks imply extensive use of such models for analytics and inferencing on large volume of data, including sensor observations, images, speech. A growing need for such inferencing to be run on devices closer to the data sources, i.e. devices which reside at the edge of the network, popularly known as fog devices exists, in order to reduce the upstream network traffic. However, being computationally constrained in nature, executing complex deep inferencing models on such devices has been proved difficult. This has led to several new approaches to partition/distribute the computation and/or data over multiple fog devices. In this paper we propose a novel depth-wise input partitioning scheme for CNN models and experimentally prove that it achieves better performance compared to row/column or grid based schemes.