A coarse-grained Reconfigurable Processing Unit (RPU) consisting of 16×16 multi-functional Processing Elements (PEs) interconnected by an area-efficient Line-Switched Mesh Connect (LSMC) routing is implemented on a 5.4mm×3.1mm die in TSMC 65 nm LP1P8M CMOS technology. A Hierarchical Configuration Context (HCC) organization scheme is proposed to reduce the implementation overhead and the energy dissipation spent on fast reconfiguration. The proposed RPU is integrated into two System-on-a-Chips (SoCs), targeting multiple-standard video decoding. The high-performance chip, comprising two RPU processors (named REMUS_HPP), can decode 1920×1080 H.264 video streams at 30 frames per second (fps) under 200 MHz. REMUS_HPP achieves a 25% performance gain over the XPP-III reconfigurable processor with only 280 mW power consumption, resulting in a 14.3× improvement on energy efficiency. The other chip (named REMUS_LPP), targeting low power applications, integrates only one RPU processor. REMUS_LPP can decode 720×480 H.264 video streams at 35fps with 24.5 mW under 75 MHz, achieving a 76% reduction in power dissipation and a 3.96× improvement on energy efficiency compared with the ADRES reconfigurable processor.
IndexTerms-Coarse-grained reconfigurable array, reconfigurable computing, video decoding.
According to the chaotic features and typical fractional order characteristics of the bearing vibration intensity time series, a forecasting approach based on long range dependence (LRD) is proposed. In order to reveal the internal chaotic properties, vibration intensity time series are reconstructed based on chaos theory in phase-space, the delay time is computed with C-C method and the optimal embedding dimension and saturated correlation dimension are calculated via the Grassberger-Procaccia (G-P) method, respectively, so that the chaotic characteristics of vibration intensity time series can be jointly determined by the largest Lyapunov exponent and phase plane trajectory of vibration intensity time series, meanwhile, the largest Lyapunov exponent is calculated by the Wolf method and phase plane trajectory is illustrated using Duffing-Holmes Oscillator (DHO). The Hurst exponent and long range dependence prediction method are proposed to verify the typical fractional order features and improve the prediction accuracy of bearing vibration intensity time series, respectively. Experience shows that the vibration intensity time series have chaotic properties and the LRD prediction method is better than the other prediction methods (largest Lyapunov, auto regressive moving average (ARMA) and BP neural network (BPNN) model) in prediction accuracy and prediction performance, which provides a new approach for running tendency predictions for rotating machinery and provide some guidance value to the engineering practice.
This paper presents a new approach to shortterm wind speed prediction. The chaotic time series analysis method is used to capture the characteristic of complex wind behavior in which a correlation dimension method is employed to calculate embedding dimension of the time series, then a mutual information method is used to determine the time delay. Based on the embedding dimension and time delay, support vector regression (SVR) is trained to perform the prediction. The proposed method is evaluated using the real-world data collected from a wind farm. The results have demonstrated the accuracy of the proposed wind speed prediction method in comparison with that offered by an artificial neural network (ANN).Index Terms-Wind speed, prediction model, chaotic time series, embedding theorem, the largest Lyapunov exponent, support vector regression.
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