Implementing Deep Learning and Inferencing on Fog and Edge Computing Systems

摘要

The case for leveraging the computing resources of smart devices at the edge of network was conceptualized almost nine years back. Since then several concepts like Cloudlets, Fog etc. were instrumental in realizing computing at network edge, in physical proximity to the data sources for building more responsive, scalable and available Cloud based services. An essential component in smartphone applications, Internet of Things(IoT), field robotics etc. is the ability to analyze large amount of data with reasonable latency. Deep Learning is fast becoming a de facto choice for performing this data analytics owing to its ability to reduce human interventions in such workflows. Major deterrent of providing Deep Learning based Cloud services are Cloud outages and relatively high latency. In the current article the role of Fog Computing in addressing these issues is discussed, current state of standardization in Fog / Edge Computing is reviewed and the importance of optimum resource provisioning for running Edge-Analytics is highlighted. A detailed design and evaluation of the distribution and parallelization aspects of an Edge based Deep Learning framework using off-the-shelf components is presented along with strategies for optimum resource provisioning in constrained edge devices based on experiments with system resource (CPU, GPU & RAM) consumptions of a Deep Convolutional Neural Network.