mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications

Abstract

As the Internet of Things (IoT) is becoming more pervasive in our daily lives, the number of devices that connect to IoT edges and data generated at the edges are rapidly increasing. On account of the bottlenecks in servers, due to the increase in data, as well as security and privacy issues, the IoT paradigm has shifted from cloud computing to edge computing. Pursuant to this trend, embedded devices require complex computation capabilities. However, due to various constraints, edge devices cannot equip enough hardware to process data, so the flexibility of operation is reduced, because of the limitations of fixed hardware functions, relative to cloud computing. Recently, as application fields and collected data types diversify, and, in particular, applications requiring complex computation such as artificial intelligence (AI) and signal processing are applied to edges, flexible processing and computation capabilities based on hardware acceleration are required. In this paper, to meet these needs, we propose a new IoT platform, called a metamorphic IoT (mIoT) platform, which can various hardware acceleration with limited hardware platform resources, through on-demand transmission and reconfiguration of required hardware at edges instead of via transference of sensing data to a server. The proposed platform reconfigures the edge’s hardware with minimal overhead, based on a probabilistic value, known as callability. The mIoT consists of reconfigurable edge devices based on RISC-V architecture and a server that manages the reconfiguration of edge devices based on callability. Through various experimental results, we confirmed that the callability-based mIoT platform can provide the hardware required by the edge device in real time. In addition, by performing various functions with small hardware, power consumption, which is a major constraint of IoT, can be reduced.

An Efficient On-Demand Hardware Replacement Platform for Metamorphic Functional Processing in Edge-Centric IoT Applications

“…Since each IoT edge operation changes depending on the situation, the average value of the entire system is calculated to obtain the average reconfiguration time overhead. Therefore, the sum of the reconfiguration time overhead t recon consumed at every edge is divided by the sum of the number of reconfiguration events E total_recon occurring at every edge [33]. This is about 5% faster than 0.22 s, which was the result obtained using the same configuration in the previous server-centric mIoT, but it cannot be clearly established that the reconfiguration overhead decreased due to the characteristic of large-scale edges operating independently and accessing the server irregularly.…”

“…The mIoT platform is an IoT platform that has metamorphism that can change the hardware configuration of the edge in real time depending on the situation [33]. The dictionary definition of metamorphism is "(of rock) changed into a new form and structure by very great heat and pressure;" in other words, its structure and shape are changed by the external environment.…”

“…The first mIoT proposed in the author's previous study was a server-centric mIoT platform [33]. The edges transmitted all the operational information needed to predict the next reconfiguration to the edge server, and all the reconfiguration tasks, such as next reconfiguration predictions and callability LUT updates, were processed by the edge server.…”

Metamorphic Edge Processor Simulation Framework Using Flexible Runtime Partial Replacement of Software-Embedded Verilog RTL Models

“…When the system emulator and RTL sessions communicate through a virtual layer, the port is opened using a shared library that depends on the parameters. To possess metamorphism, system emulator and simulator can replace their components by dynamically loads different shared libraries for each parameter set, and the code corresponding to the RTL function in the application does not require a change or rebuild, only a change of the shared library to load [20]- [23]. Thus, we can quickly change the parameter set at runtime to debug the system-level models and simulate RTL IP.…”

Energy-Efficient FPGA Accelerator With Fidelity-Controllable Sliding-Region Signal Processing Unit for Abnormal ECG Diagnosis on IoT Edge Devices

“…After the learning process, the server synthesizes and implements a hardware accelerator that can diagnose the ECG signal on the field-programmable gate array (FPGA) in real-time using a personalized reference signal. The proposed platform reduces the amount of data analyzed during the learning and diagnosing process by adjusting fidelity, increases the detection rate of ECG signals different for each person using a reference signal optimized for individuals, and processes multiple ECG signals at the same time to implement a diagnostic unit flexibly using an FPGA [11].…”