Role of FPGA in Internet of Things Applications

Elnawawy, Mohammed; Farhan, Abid; Nabulsi, Ahmad Al; Al-Ali, A. R.; Sagahyroon, Assim

doi:10.1109/isspit47144.2019.9001747

Cited by 33 publications

(16 citation statements)

References 10 publications

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“…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]. These topics open discussion not only for IoT FPGA applications, but also for the various methods of interfacing an FPGA with the Internet.…”

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

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FPGA Remote Laboratory Using IoT Approaches

Magyari

Chen

2021

Electronics

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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.

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Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

“…A popular method for providing an IoT interface with an FPGA is the Raspberry Pi [14,20]. A wireless monitoring system, proposed by Gophane et al, utilizes a combination of sensors alongside a Spartan 6 FPGA working in series with a Raspberry Pi 3 [20].…”

Section: Fpga Iot Integration Methods and Applicationsmentioning

confidence: 99%

FPGA Remote Laboratory Using IoT Approaches

Magyari

Chen

2021

Electronics

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“…Field programmable gate array (FPGA) is a device that has typical function in digital logic [7], [8]. FPGA-based system has been applied to various applications, such as in microelectronics, industrial control, robotics, and internet of things (IoT) [9]- [11]. According to Deepchand's research, FPGA is able to perform multi-channel universal asynchronous receiver transmitter (UART) communication processes and multi-channel pulse-width modulation (PWM) generator [12], [13].…”

Section: Figure 1 Prototype Using 4 Gps Receiversmentioning

confidence: 99%

Design of field programmable gate array-based data processing system for multi global positioning system receiver

Abidin

Aryawiratama

Muttaqin

et al. 2022

IJECE

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<span>A global positioning system (GPS) sensor is needed for a ballistic/moving object to do position tracking. In previous study, a multi GPS processing system was made using several microcontrollers and data processing cannot be done simultaneously. Therefore, it was considered as ineffective system. In this research, field programmable gate array (FPGA)-based data processing system for multi-GPS receiver was proposed. The proposed system was designed to reduce root mean square error (RMSE). There are two main processes in the proposed system which work in parallel, i.e. data parsing and data processing. Raw data from GPS receiver was collected and calculated to get average value, then sent it through serial communication to show result. Experimental results <span>confirm the RMSE value of the proposed system is smaller than the conventional one. The RMSE for latitude, longitude, and altitude decrease by 38.46%, 58.28%, and 24.</span>80%, respectively.</span>

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“…Consequently, GPUs are not widely used for embedded edge computing [16]. On the other hand, FPGA-based acceleration solutions have proven to be as close as some high-end GPUs while maintaining a low power consumption; this explains their popularity in this domain [14,17].…”

Section: Related Workmentioning

confidence: 99%

A Novel Automate Python Edge-to-Edge: From Automated Generation on Cloud to User Application Deployment on Edge of Deep Neural Networks for Low Power IoT Systems FPGA-Based Acceleration

Belabed

Silva

Quenon

et al. 2021

Sensors

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Deep Neural Networks (DNNs) deployment for IoT Edge applications requires strong skills in hardware and software. In this paper, a novel design framework fully automated for Edge applications is proposed to perform such a deployment on System-on-Chips. Based on a high-level Python interface that mimics the leading Deep Learning software frameworks, it offers an easy way to implement a hardware-accelerated DNN on an FPGA. To do this, our design methodology covers the three main phases: (a) customization: where the user specifies the optimizations needed on each DNN layer, (b) generation: the framework generates on the Cloud the necessary binaries for both FPGA and software parts, and (c) deployment: the SoC on the Edge receives the resulting files serving to program the FPGA and related Python libraries for user applications. Among the study cases, an optimized DNN for the MNIST database can speed up more than 60× a software version on the ZYNQ 7020 SoC and still consume less than 0.43W. A comparison with the state-of-the-art frameworks demonstrates that our methodology offers the best trade-off between throughput, power consumption, and system cost.

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Role of FPGA in Internet of Things Applications

Cited by 33 publications

References 10 publications

FPGA Remote Laboratory Using IoT Approaches

FPGA Remote Laboratory Using IoT Approaches

Design of field programmable gate array-based data processing system for multi global positioning system receiver

A Novel Automate Python Edge-to-Edge: From Automated Generation on Cloud to User Application Deployment on Edge of Deep Neural Networks for Low Power IoT Systems FPGA-Based Acceleration

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