Introducing Hardware-Based Intelligence and Reconfigurability on Industrial IoT Edge Nodes

In this article, we present a system architecture that can handle the overall manufacturing chain of an Industry 4.0 plant and divide it into different layers, focusing on the level of intelligence that each layer can handle. For this system, we propose a mechanism that can efficiently migrate IloT computational functionality and edge intelligence to the system end node devices. Since such devices do not always have the resources to support complex operations, we propose the executions of these operations through hardware means inside the node SoC. To bypass the lack of flexibility that ASICs have, we propose the use of FPGA technology on the end -node device. Recent advancements in FPGA technology enable us to use SoC core designs that merge powerful Advanced RISC Machine (ARM) -embedded processors with FPGA fabric. As an example of our proposed approach, we introduce a prototype concept of an edge node for our system that has hardware support for a statistical feature extraction mechanism and for detecting outlier behavior through novelty vector extraction using the extracted features and a training set of known good values. The nearest neighbor machine-learning algorithm is used for novelty extraction. Using this concept, we can support condition monitoring operations for industrial assets on the edge level. This approach was prototyped on an actual embedded system setting (Smartfusion2 SoC board combined with the CC2538-based Openmote board for wireless communication) and the two procedures (i.e., feature and novelty extraction) were realized in pure software and the proposed hardware or software codesigned way (using the Smartfusion2 SoC FPGA fabric). The experimental results confirmed that the proposed approach had considerable savings in consumed energy and led to very small time delays compared to traditional purely software solutions.