2021
DOI: 10.3390/electronics10080952
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GPU-Based Embedded Intelligence Architectures and Applications

Abstract: This paper present contributions to the state-of-the art for graphics processing unit (GPU-based) embedded intelligence (EI) research for architectures and applications. This paper gives a comprehensive review and representative studies of the emerging and current paradigms for GPU-based EI with the focus on the architecture, technologies and applications: (1) First, the overview and classifications of GPU-based EI research are presented to give the full spectrum in this area that also serves as a concise summ… Show more

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Cited by 14 publications
(7 citation statements)
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References 79 publications
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“…Parallel processors still have standard architectures and are not explicitly intended for artificial intelligence applications, but they perform much better in this domain as compared to classic processors. Most of these architectures use Very Long Instruction Words (VLIW), consisting of Single Instruction Multiple Data (SIMD), an important advantage for the implementation of artificial intelligence algorithms, which require a large number of repetitions of the same operations for distinct data [ 6 ]. Risks in use remain low, but, in comparison with CPUs, flexibility in use decreases a little due to specific resource programming requirements.…”
Section: Related Workmentioning
confidence: 99%
“…Parallel processors still have standard architectures and are not explicitly intended for artificial intelligence applications, but they perform much better in this domain as compared to classic processors. Most of these architectures use Very Long Instruction Words (VLIW), consisting of Single Instruction Multiple Data (SIMD), an important advantage for the implementation of artificial intelligence algorithms, which require a large number of repetitions of the same operations for distinct data [ 6 ]. Risks in use remain low, but, in comparison with CPUs, flexibility in use decreases a little due to specific resource programming requirements.…”
Section: Related Workmentioning
confidence: 99%
“…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%
“…This section provides a discussion on hardware and devices for multimedia on edge intelligence. The reader can refer to the survey papers in [79,80] for further works on GPU and FPGA-embedded intelligence systems. Edge-based hardware devices for the deployment of AI and machine learning can be classified into the following types: (1) Application-Specific Integrated Circuit (ASICs) Chips-ASICs for AI applications are designed specifically to execute machine/deep learning algorithms and have the advantages of being compact in size with low power consumption.…”
Section: Hardware and Devices For Multimedia On Edge Intelligencementioning
confidence: 99%