Performance Analysis of Modified Gram-Schmidt Cholesky Implementation on 16 bits-DSP-chip

Maoudj, Rabah; Fety, Luc; Alexandre, Christophe

doi:10.12785/ijcds/020103

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…Several methods of decomposition are available from numerous studies (Bellantoni and Dodge 1967;Gonzalez et al 2014;Roncero 2014;Thornton 1976). In this study, we employ the Bierman-Thornton algorithm (see appendix A), which uses modified Cholesky factors of the state variance-covariance matrix, since the modified Cholesky decomposition has been found to be more robust than the classical one (Maoudj et al 2013). It is acts as a square root filter, though it does not use any square root operations in its implementation.…”

Section: B Kalman Filtermentioning

confidence: 99%

A Time-Varying Causality Formalism Based on the Liang–Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems

et al. 2019

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The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang–Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its time-invariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture–air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions.

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Section: B Kalman Filtermentioning

confidence: 99%

A Time-Varying Causality Formalism Based on the Liang–Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems

et al. 2019

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“…He applied the dedicated structure in compressed sensing reconstruction further [4]. However, it is normally expensive for a GPU and its performance is limited for a CPU [1] or a DSP approach [5,6]. In [7], a software-and hardware co-design has been used in matrix manipulation with hardware taken as acceleration.…”

Section: Introductionmentioning

confidence: 99%

High throughput Cholesky decomposition based on FPGA

Luo

Huang

Chang

et al. 2013

2013 6th International Congress on Image and Signal Processing (CISP)

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Cholesky decomposition has wide applications in solving many engineering and scientific problems. Acceleration is an important issue in many of these problems. In this paper, a hardware-based LL T Cholesky decomposition featuring high throughput has been presented to solve wiener filtering based on the minimum square error criterion. To achieve the best efficiency, the hardware-based implementation has been realized by fixed-point multiple structures and various pipeline stages. Parallel properties have been exploited to improve the throughput. Results have shown that a significant speedup has been achieved compared to the software-based approach.

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Efficient Implementation of Givens QR Decomposition on VLIW DSP Architecture for Orthogonal Matching Pursuit Image Reconstruction

Najoui

Hatim

Bahtat

et al. 2016

Lecture Notes in Electrical Engineering

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Performance Analysis of Modified Gram-Schmidt Cholesky Implementation on 16 bits-DSP-chip

Cited by 6 publications

References 7 publications

A Time-Varying Causality Formalism Based on the Liang–Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems

A Time-Varying Causality Formalism Based on the Liang–Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems

High throughput Cholesky decomposition based on FPGA

Efficient Implementation of Givens QR Decomposition on VLIW DSP Architecture for Orthogonal Matching Pursuit Image Reconstruction

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