Edge Computing for Internet of Things Based on FPGA

Ferdian, Rian; Aisuwarya, Ratna; Erlina, Tati

doi:10.1109/icitsi50517.2020.9264937

Cited by 9 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While integrating FPGAs onto the IoT is far from a trivial task, FPGAs have a variety of uses and implementations as an IoT device [14][15][16][17][18][19]. IoT ready FPGAs are currently being utilized for online methods digital image processing, smart grid energy management, managing complex timing systems, broadcasting video recording solutions, data encryption, medical diagnoses and monitoring, and storage system solutions [14].…”

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

“…One further application of an FPGA in the realm of IoT is edge computing, which was researched by Ferdian et al [16]. By relying on an FPGA with IoT access, rather than a conventional microprocessor, Ferdian et al utilized an FPGA as an IoT node to process and encrypt data before transmitting it.…”

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

“…By relying on an FPGA with IoT access, rather than a conventional microprocessor, Ferdian et al utilized an FPGA as an IoT node to process and encrypt data before transmitting it. The edge computing provided by the FPGA is able to reduce the needed data bandwidth by 66% by encrypting and compressing the data as compared to raw, unprocessed data [16]. Further, as the FPGA can process data in parallel, Ferdian proved that the processing speed of the FPGA remained constant even when presented with an increasing amount of sensor [16].…”

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

“…The edge computing provided by the FPGA is able to reduce the needed data bandwidth by 66% by encrypting and compressing the data as compared to raw, unprocessed data [16]. Further, as the FPGA can process data in parallel, Ferdian proved that the processing speed of the FPGA remained constant even when presented with an increasing amount of sensor [16].…”

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

See 3 more Smart Citations

FPGA Remote Laboratory Using IoT Approaches

Magyari

Chen

2021

Electronics

View full text Add to dashboard Cite

Field-Programmable Gate Arrays (FPGAs) are relatively high-end devices that are not easily shared between multiple users. In this work, we achieved a remotely accessible FPGA framework using accessible Internet of Things (IoT) approaches. We sought to develop a method for students to receive the same level of educational quality in a remote environment that they would receive in a typical, in-person course structure for a university-level digital design course. Keeping cost in mind, we are able to combine the functionality of an entry-level FPGA and a Raspberry Pi Zero to provide IoT access for laboratory work. Previous works in this field allow only one user to access an FPGA at a time, which requires students to schedule time slots. Our design is unique in that it gives multiple users the ability to interact simultaneously with one individual top-level design on an FPGA. This novel design has the benefit for classroom presentations, collaboration and debugging, and eliminates the need for restricting student access to a time slot for FPGA access. Further, our hardware wrapper is lightweight, utilizing less than 1% of tested FPGA chips, allowing it to be integrated with resource-heavy designs. The application is meant to scale with large designs; there is no difference between how many users can interact with the remote design, regardless of the complexity of the design. Further, the number of users who can interact with a single project is limited only by the bandwidth restrictions imposed by Google Fire Base, which is far beyond any practical number of users for simultaneous access.

show abstract

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

FPGA Remote Laboratory Using IoT Approaches

Magyari

Chen

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…Thus, the edge cannot reduce processing delays without reducing network and server dependency. On the other hand, if all calculations are processed on an edge, as mentioned before, processing delays also occur due to the poor performance and flexibility of the edge [31,32]. Therefore, this paper proposes a metamorphic IoT platform that can reduce server workload and network dependency compared to the cloud-based system and increase the processing performance and flexibility compared to the edge-based IoT system.…”

Section: Metamorphic Iot Platform (Miot)mentioning

confidence: 99%

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

Moon

Park

2021

Electronics

View full text Add to dashboard Cite

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.

show abstract