Partial Dynamic Reconfiguration for efficient Adaptive Implementation of a Video Shot Boundary Detection System

Tej, Balkis; Hannachi, Marwa; Abdelali, Abdessalem Ben

doi:10.1109/ic_aset49463.2020.9318313

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…In a paper by Tej et al (2020), the authors offer a real-time development of a video shot border detection system on FPGA using the Local Histogram (LH) and Color Structure Descriptor (CSD). These identifiers have been implemented in a few distinct ways, with two for the LH and three for the CSD (32, 16, and 8 quantization levels respectively) (8 and 4 quantization levels).…”

Section: Literature Reviewmentioning

confidence: 99%

“…It offers low-cost flexibility and computational power. However, since the start of the decade, processor core frequency has stopped increasing while power consumption has increased significantly with performance [1,2]. This power wall causes embedded systems, particularly those used for battery-powered applications, to have uncontrollable temperature and autonomy problems.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Reconfiguration Using Signal Processing for Artificial Intelligence

2024

nano-ntp

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A newly developed meta-surface reconfigurable intelligent surface has been singled out as a top choice for 6G mobile communications networks. By reflecting signals, RIS might effectively install new communication conduits. Meanwhile, it has been widely accepted in recent years that AI may be a useful tool. With the help of artificial intelligence (AI), RIS communications will be more capable of adapting to new situations and more resilient to interference. Artificial intelligence (AI) may tackle contemporary signal processing problems in a data-driven manner by extracting the inherent features from the real data, in addition to the conventional model-driven methodologies. Because of this, AI is well-suited for the digital signal problems through RIS networks under less-than-ideal settings such as modeling mismatch, a shortage of resources, hardware deterioration, and dynamic transmissions. As one of the first survey articles on the topic, we will discuss the application of AI and RIS, or artificial intelligence and radio frequency identification systems, to a wide range of signal processing tasks, including environmental sensing, channel acquisition, directional antenna design, bandwidth allocation, etc. In addition, we'll discuss the challenges of AIRIS and provide some promising future directions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Reconfiguration Using Signal Processing for Artificial Intelligence

2024

nano-ntp

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“…In general, the entire hardware is not activated at the same time. Therefore, if a module with less probability of being used could be replaced with another module, it would be possible to perform more diverse operations with smaller hardware resources [24][25][26]. DPR techniques can be classified depending on whether or not an external flash memory is used to store partial bitstreams.…”

Section: Dynamic Partial Reconfiguration (Dpr)mentioning

confidence: 99%

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

Moon

Park

2021

Electronics

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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.

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Partial Dynamic Reconfiguration for efficient Adaptive Implementation of a Video Shot Boundary Detection System

Cited by 2 publications

References 9 publications

Dynamic Reconfiguration Using Signal Processing for Artificial Intelligence

Dynamic Reconfiguration Using Signal Processing for Artificial Intelligence

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

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