GPU-Based Embedded Intelligence Architectures and Applications

Ang, Li Minn; Seng, Kah Phooi

doi:10.3390/electronics10080952

Cited by 13 publications

(7 citation statements)

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%

Hardware–Software Partitioning for Real-Time Object Detection Using Dynamic Parameter Optimization

Zaharia

Popescu

Sandu

2023

Sensors

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Computer vision algorithms implementations, especially for real-time applications, are present in a variety of devices that we are currently using (from smartphones or automotive applications to monitoring/security applications) and pose specific challenges, memory bandwidth or energy consumption (e.g., for mobility) being the most notable ones. This paper aims at providing a solution to improve the overall quality of real-time object detection computer vision algorithms using a hybrid hardware–software implementation. To this end, we explore the methods for a proper allocation of algorithm components towards hardware (as IP Cores) and the interfacing between hardware and software. Addressing specific design constraints, the relationship between the above components allows embedded artificial intelligence to select the operating hardware blocks (IP cores)—in the configuration phase—and to dynamically change the parameters of the aggregated hardware resources—in the instantiation phase, similar to the concretization of a class into a software object. The conclusions show the benefits of using hybrid hardware–software implementations, as well as major gains from using IP Cores, managed by artificial intelligence, for an object detection use-case, implemented on a FPGA demonstrator built around a Xilinx Zynq-7000 SoC Mini-ITX sub-system.

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Section: Related Workmentioning

confidence: 99%

Hardware–Software Partitioning for Real-Time Object Detection Using Dynamic Parameter Optimization

Zaharia

Popescu

Sandu

2023

Sensors

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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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“…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%

Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing

et al. 2022

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The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.

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GPU-Based Embedded Intelligence Architectures and Applications

Cited by 13 publications

References 79 publications

Hardware–Software Partitioning for Real-Time Object Detection Using Dynamic Parameter Optimization

Hardware–Software Partitioning for Real-Time Object Detection Using Dynamic Parameter Optimization

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

Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing

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