Embedded Intelligence on FPGA: Survey, Applications and Challenges

Seng, Kah Phooi; Lee, Paik Jen; Ang, Li Minn

doi:10.3390/electronics10080895

Cited by 64 publications

(28 citation statements)

References 86 publications

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“…The subject of customizing deeplearning methods to fit targeted tasks and types of hardware has been widely studied on recent years, in particular the use of graphics processing units (GPUs), field programmable gate arrays (FGPAs) and application-specific integrated circuits (ASICs), thus highlighting their pros and cons. The developments made in recent years have been analyzed and summarized by several authors: Seng et al [52] for FPGAs, Moolchandani et al [53] for ASICs and Ang et al [54] for GPUs.…”

Section: Limitations and Challengesmentioning

confidence: 99%

A Survey on Recent Advances in AI and Vision-Based Methods for Helping and Guiding Visually Impaired People

et al. 2022

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We present in this paper the state of the art and an analysis of recent research work and achievements performed in the domain of AI-based and vision-based systems for helping blind and visually impaired people (BVIP). We start by highlighting the recent and tremendous importance that AI has acquired following the use of convolutional neural networks (CNN) and their ability to solve image classification tasks efficiently. After that, we also note that VIP have high expectations about AI-based systems as a possible way to ease the perception of their environment and to improve their everyday life. Then, we set the scope of our survey: we concentrate our investigations on the use of CNN or related methods in a vision-based system for helping BVIP. We analyze the existing surveys, and we study the current work (a selection of 30 case studies) using several dimensions such as acquired data, learned models, and human–computer interfaces. We compare the different approaches, and conclude by analyzing future trends in this domain.

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Section: Limitations and Challengesmentioning

confidence: 99%

A Survey on Recent Advances in AI and Vision-Based Methods for Helping and Guiding Visually Impaired People

et al. 2022

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“…In order to implement an algorithm, the researcher can use programming and processing boards such as DSP, DSPACE, FPGA, Microcontroller [5,6,7]. Indeed, due to the incompatibility with MATLAB and interoperability issues, it is necessary to use other proprietary programming platforms to implement the developed algorithms.…”

Section: B Interoperability Issuesmentioning

confidence: 99%

Development of a Real-time Framework between MATLAB and PLC through OPC-UA: A case study of a microgrid energy management system

Zerk

Ouassaid

Zidani

2022

Preprint

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Many research laboratories, which develop advanced algorithms for controlling Renewable Energy Systems (RES), lack specialized equipment to carry out research activities and perform experiments. Real-time simulation platforms are suitable for designing specific applications such as fast or real-time computation models. In addition, these platforms provide fast, accurate and reliable studies for RES control implementation. However, Real-time platforms are too expensive and not affordable for most laboratories. In this context, a cost-effective platform is designed to assist researchers in their activities. The hardware solution consists of using a Programmable Logic Control (PLC) to acquire data from the various sensors and control the system through actuators and PID controllers. From a software standpoint, a communication framework between MATLAB and the PLC is developed using the OPC-UA (Open Platform Communications Unified Architecture) technology. In this paper, the operation and efficiency of the platform have been tested using the Software in the Loop (SIL) technique. Hence, the operating model is designed using MATLAB/Simulink to simulate and analyze the appropriate control technique suitable for PLC implementation, whereas the control is executed through Siemens S7-1200 PLC using TIA PORTAL. A visualization interface is set up using WinCC-RT. Furthermore, this paper describes the design of the platform and discusses its utilization, providing examples of its usage through a case study. Indeed, a complex algorithm developed in MATLAB/Simulink can be automatically executed by any PLC using the designed framework.

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“…Due to the limitations of embedded systems, much research in adopting AI in embedded systems concentrates on power and area optimization [ 21 , 22 , 23 , 24 ]. The research of [ 21 ] is one of the cases.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Core Controller for an Embedded AI System Supporting Parallel Recognition

Jang

Yoon

et al. 2021

Micromachines

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Recent advances in artificial intelligence (AI) technology encourage the adoption of AI systems for various applications. In most deployments, AI-based computing systems adopt the architecture in which the central server processes most of the data. This characteristic makes the system use a high amount of network bandwidth and can cause security issues. In order to overcome these issues, a new AI model called federated learning was presented. Federated learning adopts an architecture in which the clients take care of data training and transmit only the trained result to the central server. As the data training from the client abstracts and reduces the original data, the system operates with reduced network resources and reinforced data security. A system with federated learning supports a variety of client systems. To build an AI system with resource-limited client systems, composing the client system with multiple embedded AI processors is valid. For realizing the system with this architecture, introducing a controller to arbitrate and utilize the AI processors becomes a stringent requirement. In this paper, we propose an embedded AI system for federated learning that can be composed flexibly with the AI core depending on the application. In order to realize the proposed system, we designed a controller for multiple AI cores and implemented it on a field-programmable gate array (FPGA). The operation of the designed controller was verified through image and speech applications, and the performance was verified through a simulator.

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Embedded Intelligence on FPGA: Survey, Applications and Challenges

Cited by 64 publications

References 86 publications

A Survey on Recent Advances in AI and Vision-Based Methods for Helping and Guiding Visually Impaired People

A Survey on Recent Advances in AI and Vision-Based Methods for Helping and Guiding Visually Impaired People

Development of a Real-time Framework between MATLAB and PLC through OPC-UA: A case study of a microgrid energy management system

A Multi-Core Controller for an Embedded AI System Supporting Parallel Recognition

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