Optimizing a 3D-FWT Video Encoder for SMPs and HyperThreading Architectures

Fernández, Ricardo Barrón; García, José M.; Bernabé, Gregorio; Acacio, Manuel E.

doi:10.1109/empdp.2005.28

Cited by 2 publications

(2 citation statements)

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“…This makes it a key component for the 2D fast Fourier transform (FFT) in image processing and machine vision [1], multiple-input multipleoutput (MIMO) [2], [3], automotive [4] and synthetic aperture radars [5]- [7]. Likewise, it is required for the 3D FFT in molecular dynamics [8], motion detection [9]; for the 2D discrete cosine transform (DCT) in image compression [10], [11]; for the 2D fast Hartley transform (FHT) in image processing and circular convolution [12], [13]; and for the 3D fast Wavelet transform (FWT) in video encoding [14]. Additionally, matrix transposition is considered in convolutional neural networks (CNN) [15], [16] for artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Continuous-Flow Matrix Transposition Using Memories

Garrido

Pirsch

2020

IEEE Trans. Circuits Syst. I

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In this paper, we analyze how to calculate the matrix transposition in continuous flow by using a memory or group of memories. The proposed approach studies this problem for specific conditions such as square and non-square matrices, use of limited access memories and use of several memories in parallel. Contrary to previous approaches, which are based on specific cases or examples, the proposed approach derives the fundamental theory involved in the problem of matrix transposition in a continuous flow. This allows for obtaining the exact equations for the read and write addresses of the memories and other control signals in the circuits. Furthermore, the cases that involve non-square matrices, which have not been studied in detail in the literature, are analyzed in depth in this paper. Experimental results show that the proposed approach is capable of transposing matrices of 8192 x 8192 32-bit data received in series at a rate of 200 mega samples per second, which doubles the throughput of previous approaches.

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Section: Introductionmentioning

confidence: 99%

Continuous-Flow Matrix Transposition Using Memories

Garrido

Pirsch

2020

IEEE Trans. Circuits Syst. I

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“…In [13], parallel implementations of numerical and image processing applications on a multiprocessor systems have been presented. In [16] the implementation of the H.264 encoder using Intel's hyper-threading architecture has been presented, while in [8] a 3D-FWT video encoder has been implemented using both OpenMP as well as Pthreads.…”

Section: Introductionmentioning

confidence: 99%

Model-Based OpenMP Implementation of a 3D Facial Pose Tracking System

Saha

Shen

Hsu

et al.

2006 International Conference on Parallel Processing Workshops (ICPPW'06)

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Most image processing applications are characterized by computation-intensive operations, and high memory and performance requirements. Parallelized implementation on shared-memory systems offer an attractive solution to this class of applications. However, we cannot thoroughly exploit the advantages of such architectures without proper modeling and analysis of the application. In this paper we describe our implementation of a 3D facial pose tracking system using the OpenMP platform. Our implementation is based on a design methodology that uses coarse-grain dataflow graphs to model and schedule the application. We present our modeling approach, details of the implementation that we derived based on this modeling approach, and associated performance results. The parallelized implementation achieves significant speedup, and meets or exceeds the target frame rate under various configurations.

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Optimizing a 3D-FWT Video Encoder for SMPs and HyperThreading Architectures

Abstract: Abstract

Cited by 2 publications

References 20 publications

Continuous-Flow Matrix Transposition Using Memories

Continuous-Flow Matrix Transposition Using Memories

Model-Based OpenMP Implementation of a 3D Facial Pose Tracking System

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