In this paper we discuss the choice of objective function in list scheduling algorithms for scheduling dataflow graphs onto multiprocessor architectures. A majority of the list scheduling algorithms used in practice u...
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In this paper we discuss the choice of objective function in list scheduling algorithms for scheduling dataflow graphs onto multiprocessor architectures. A majority of the list scheduling algorithms used in practice utilize a global strategy wherein actor static levels are used for making scheduling decisions. When fine grain DSP applications such as FIR or elliptical filters need to be scheduled on architectures that consist of commodity part processors and a general interconnection network whose interprocessor communication cost cannot be ignored, a traditional list scheduling algorithm is in many cases not the best choice. In an experimental study we compare these global strategies to local strategies that utilize load balancing. The study reveals that global strategies suffer from flaws that could cause local strategies to yield more than 10% shorter schedule lengths on the average. In particular we find that a novel Earliest Finish Time (EFT) strategy exhibits very good performance.
作者：
J.S. CastroP. Baylou351
Cours de la Libération Equipe Signal et Image ENSER Bordeaux Talence France
A comparison is made of techniques of parallelization applied to iterative and recursive algorithms. These algorithms are analyzed considering three criteria to generate schedules. These schedules associated with a Bo...
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A comparison is made of techniques of parallelization applied to iterative and recursive algorithms. These algorithms are analyzed considering three criteria to generate schedules. These schedules associated with a Boolean network model, allow the detection of communication conflicts. To reduce conflict possibility, the third criterion is proposed to take into consideration the communication constraints and their influence on the multiprocessor architecture.< >
Previously published results demonstrated that a sequential combination of adaptive linear prediction configuration (LPC), adaptive noise cancellation (ANC), and adaptive comb filtering (CF) can effectively remove mat...
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ISBN:
(数字)9798350354058
ISBN:
(纸本)9798350354065
Previously published results demonstrated that a sequential combination of adaptive linear prediction configuration (LPC), adaptive noise cancellation (ANC), and adaptive comb filtering (CF) can effectively remove maternal interference from noninvasive fetal ECGs. More recent research results have verified that the bio-inspired Lévy Flight Firefly Algorithm (LFFA) can be effectively applied to many different forms of linear and non-linear adaptive filter structures, including low sensitivity IIR adaptive structures that could not be used with steepest descent adaptive algorithms due to their adaptive multimodal characteristics. This paper presents results when using the LFFA algorithm in various stages of the FECG sequential combination processing.
Advanced adaptive signal processing algorithms commonly used in optimization, filtering, and estimation are subjects of seeking innovations and advancements. The important performance factors of algorithms are adaptat...
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ISBN:
(纸本)9781538677421
Advanced adaptive signal processing algorithms commonly used in optimization, filtering, and estimation are subjects of seeking innovations and advancements. The important performance factors of algorithms are adaptation or convergence (the probability of, and the speed of), and computational cost (proportional to the algorithm complexity). The improvement in convergence often comes with higher computational needs. In this report we exploit the diversity in algorithms by using cooperative processes. An odd number of cooperative processes running simultaneously participate in a simple majority-vote decision method to improve the convergence speed of the corresponding application. The iterative algorithms concurrently progressing require higher computational resources, however only the most computationally-efficient algorithm remains active after the initial convergence is achieved. Additional technique is essential to synchronize the process delays of participating algorithms.
A novel approach to the design of frequency deviation measurement algorithms is defined. This approach is based on a signalprocessing scheme that uses quadratic forms of signal samples. The approach is general, and m...
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A novel approach to the design of frequency deviation measurement algorithms is defined. This approach is based on a signalprocessing scheme that uses quadratic forms of signal samples. The approach is general, and methods to use it as a design tool for developing new algorithms are presented. The performance of an algorithm developed to illustrate the design approach is evaluated using computer simulation tests.< >
Three dimensional beamforming (3DBF) is a famed technology that enhances spectral efficiency, capacity and coverage area of the 5G networks compared to conventional two dimensional (2D) beamforming by implementing bot...
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ISBN:
(纸本)9781538666876
Three dimensional beamforming (3DBF) is a famed technology that enhances spectral efficiency, capacity and coverage area of the 5G networks compared to conventional two dimensional (2D) beamforming by implementing both user specific horizontal and vertical beamforming. In this paper, three LMS based adaptive spatial filtering algorithms, namely, Ang's, Mathew's and Fixed step-size LMS have been implemented and evaluated on FPGA for 3DBF scenarios. The associated signalprocessing issues, specifically for FPGA implementation have been critically investigated. A prototype design of a 3DBF system deploying these adaptive algorithms has been demonstrated using Xilinx Zynq®-7000 family based FPGA platform. Coexistence of Xilinx System Generator ™ and MATLAB Simulink® environment has been suitably utilized for the purpose. The performance metrics in terms of convergence properties like Mean Square Error (MSE) and Step-size along with beam pattern of all algorithms have been evaluated. Finally, hardware resource utilization of these algorithms has also been analyzed. Investigations reveal the superiority of Mathew's algorithm over the other two for 3DBF.
New algorithms are being developed in the radar community that blend a priori knowledge source processing with traditional digital signalprocessing concepts. This operational blend necessitates a system-level archite...
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New algorithms are being developed in the radar community that blend a priori knowledge source processing with traditional digital signalprocessing concepts. This operational blend necessitates a system-level architecture capable of delivering both high processing throughput and memory bandwidth. This paper derives these system parameters from the knowledge aided pre-whitening algorithm and evaluates the performance of two high performance embedded computing architectures, the Imagine and Raw processors, on these kernels. The implementation results are compared with the measured performance of a conventional system based on the PowerPC with Altivec. The results show these processors exhibit significant improvements over conventional systems and that each architecture has its own strengths and weaknesses.
A class of subspace-based methods for estimating the direction-of-arrival (DOA) of plane waves impinging on an array of sensors is proposed. The methods estimate the DOA using only linear transformations of the data. ...
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A class of subspace-based methods for estimating the direction-of-arrival (DOA) of plane waves impinging on an array of sensors is proposed. The methods estimate the DOA using only linear transformations of the data. This is of special interest for cases when the number of sensors is large and the computational advantages of these methods are significant. These methods use a less restrictive noise model and, e.g., can accommodate cases where the noise variance is different for different sensors. Large sample variance expressions for the estimates of the DOAs are derived, and the statistical properties of the proposed method are compared against the properties of multiple signal classification (MUSIC).< >
Medical signals are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease evolution. Medical imaging mainly treats and processes missing, ambiguous, comp...
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Medical signals are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease evolution. Medical imaging mainly treats and processes missing, ambiguous, complementary, redundant, contradictory, distorted data, and information has a strong structural character. This paper reports two “immune algorithms” based approaches for medical signalprocessing. Immune algorithms belong to the Artificial Immune Systems field. The first proposed approach uses the Clonal Selection Algorithm (CSA) for geometric transform estimation in image registration (IR). The second approach, the Dendritic Cell Algorithm (DCA) is used for automatic driver stress detection using biomedical signals.
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