Abstract
The paradigm of Internet-of-things (IoT) systems is changing from a cloud-based system to an edge-based system. These changes were able to solve the delay caused by the rapid concentration of data in the communication network, the delay caused by the lack of server computing capacity, and the security issues that occur in the data communication process. However, edge-based IoT systems performance was insufficient to process large numbers of data due to limited power supply, fixed hardware functions, and limited hardware resources. To improve their performance, application-specific hardware can be installed in edge devices, but performance cannot be improved except for specific applications due to a fixed function of an application-specific hardware. This paper introduces an edge-centric metamorphic IoT (mIoT) platform that can use various hardware modules through on-demand partial reconfiguration, despite the limited hardware resources of edge devices. In addition, this paper introduces an RISC-V based metamorphic IoT processor (mIoTP) with reconfigurable peripheral modules. We experimented to prove that the proposed structure can reduce the server access of edges and can be applied to a large-scale IoT system. Experiments were conducted in a single-edge environment and a large-scale environment combining one physical edge and 99 virtual edges. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration prediction algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to our previous study in which the prediction process was executed at the server. Furthermore, we confirmed that there is no additional reconfiguration time overhead even for the large IoT systems.
Highlights
The metamorphic IoT processor (mIoTP) proposed in this paper is the RISC-V based on an on-demand reconfigurable processor that was designed in the field-programmable gate array (FPGA)
It proposes a three-layer structure consisting of edges, edge servers, a main server, and a callability-based bitstream caching algorithm (BCA) in order to reduce the overhead required for on-demand reconfiguration of the edges
Server access can be reduced by up to 82.2%, even when the code size increases by 352 bytes (8.6% of the instruction cache’s size), as the BCA was added to the edge application
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Applications that need to process large numbers of data such as artificial intelligence (AI) and healthcare have been increasing recently [3,4]; large numbers of data were transmitted to the server due to the lack of computing power at the edge Due to these changes, delays due to network congestion, bandwidth limitations, and server workload limitations are occurring [5,6]. The callability is a probabilistic value indicating which hardware will be called in the processor’s operation Through these techniques, effective reconfiguration management at the server and reduction of reconfiguration time overhead were possible in a large-scale IoT system.
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