Abstract
Artificial Intelligence (AI) technology is a huge opportunity for the Industrial Internet of Things (IIoT) in the fourth industrial revolution (Industry 4.0). However, most AI-driven applications need high-end servers to process complex AI tasks, bringing high energy consumption to IIoT environments. In this article, we introduce intelligent edge computing, emerging technology to reduce energy consumption in processing AI tasks, to build green AI computing for IIoT applications. We first propose an intelligent edge computing framework with a heterogeneous architecture to offload most AI tasks from servers. To enhance the energy efficiency of various computing resources, we propose a novel algorithm to optimize the scheduling for different AI tasks. In the performance evaluation, we build a small testbed to show the AI-driven IIoT applications’ energy efficiency with intelligent edge computing. Meanwhile, extensive simulation results show that the proposed online scheduling strategy consumes less than 80% energy of the static scheduling and 70% of the first-in, first-out (FIFO) strategy in most settings.
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