A Fuzzy Logic Based Power‐Efficient Run‐Time Reconfigurable Multicore System

Najam, Shaheryar; Muhammad, Yasir Qadri; Najam, Zohaib; Jameel, Ahmed; Nawaz, Nadia

doi:10.1049/cje.2018.02.005

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…A large number of CGRA instances [6][7][8][9][10][11][12][13][14][15][29][30][31][32][33][34][35][36] have shown that dynamically reconfigurable chips possess the characteristics of high energy efficiency and flexibility. Their energy efficiency is about hundreds of times that of General-purpose processors (GPPs) and dozens of times that of FPGAs at the same manufacture technology [9] .…”

Section: Characteristics and Reasons Analysismentioning

confidence: 99%

“…These subtasks can be classified into three categories: bitlevel arithmetic operations (entropy decoding and IQ), block-based word-level calculations (IDCT, MC, intraprediction, reconstruction and deblocking filtering), and 2-D image data reading/writing (frame memory accessing operations). The block-based word-level calculations comprise a majority of the decoding workloads (more than 75% [15] ). This feature makes the reconfigurable fabric that provides a great number of PEs an ideal platform for video decoding.…”

Section: Multimediamentioning

confidence: 99%

“…By contrast, Coarse-grained reconfigurable architecture (CGRA) is a dynamically reconfigurable fabric that needs less configuration data and shorter configuration time. Many studies [5][6][7][8][9][10][11][12][13][14][15][16][17] have shown that CGRAs have both characteristics of high energy-efficiency and flexibility. They support high-level language (i.e., C) programming and provide near-ASIC energy-efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Principle and Progress of Dynamically Reconfigurable Computing Technologies

Wei

2020

Chin. j. electron.

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Section: Characteristics and Reasons Analysismentioning

confidence: 99%

Section: Multimediamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Principle and Progress of Dynamically Reconfigurable Computing Technologies

Wei

2020

Chin. j. electron.

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“…Furthermore, the capability of fuzzy inference system to interpret linguistic rules and to defuzzify the result to a crisp set of values, without deploying a sophisticated mathematical model has made fuzzy logic a smart tool for attaining the reconfiguration of energy and performance aware multi/man core system. In previous work, Shaheryar et al [30] presented the fuzzy logic type-1 based optimizing controller for MPSoC and results were significantly better than available governors of Ubuntu operating system. However, results predicted even better response for GP-GPU based HPC units like Nvidia TK1 GPU kit.…”

Section: B Working Of Fbrmc-2mentioning

confidence: 99%

Run-Time Resource Management Controller for Power Efficiency of GP-GPU Architecture

Najam

Ahmed

Masood³

et al. 2019

IEEE Access

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The demand for high-performance computing (HPC) has been increasing since the invention of computing technology. This led to the invocation of sophisticated multi/many-core processors with high performance. Graphical processing units (GPUs) have emerged as an important innovation in the manycore era as it features a high number of processors. The GPU acts as a computational accelerator that can significantly reduce the computational time of the HPC, as it can offer standout parallelism for high-end computing applications such as graphics designing. However, increasing the resources has resulted in higher power consumption and heat dissipation which has been a challenging problem for modern HPC Units. On the other hand, because of the dynamic nature of workload, a large amount of parallelism, offered by these many-core processors, is often underutilized. An ideal system would be smart enough to efficiently utilize resources and save power where less workload is available. Reducing the resources dynamically has direct implications on the performance of the system. However, if less workload is available, reducing the resources would not harm the performance, rather it would save power with less to no trade-off in overall throughput of the system. In this paper, a smart power and performance efficient resource management controller for general purpose-GPU architecture is presented. The proposed controller, based on a feedback mechanism, keeps on analyzing the current frequency of central processing unit (CPU) and GPU, number of active cores of the CPU and utilization of CPU and GPU. On the basis of collected data, the proposed controller which features fuzzy type-2 as an optimizing mechanism tries to create a balance between performance and power consumption. The results are evaluated against various benchmarks on NVIDIA TK1 GPU kit and by using dynamic voltage and frequency scaling and core gating, up to 47 % reduction in power consumption has been achieved.

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“…As a result, it is necessary to quantify the causal relation between faults and events based on historical aviation incident records and experts' experiences, and find all the causes of failure hidden behind aviation events in time. Some representative AIbased diagnostic methods, such as machine learning-based method [4][5][6][7][8] , expert system-based method [9][10][11] , fuzzy logic-based method [12][13][14] , have achieved great success in many fields. Nevertheless, these methods either rely too much on big data or experts' experiences, and lack a unified theoretical basis and a computational model.…”

Section: Introductionmentioning

confidence: 99%