Real-Time Implementation of the Vertex Component Analysis Algorithm on GPUs

Barberis, A.; Danese, Giovanni; Leporati, Francesco; Torti, Emanuele

doi:10.1109/lgrs.2012.2200452

Cited by 35 publications

(19 citation statements)

References 18 publications

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“…CUDA Basic Linear Algebra Subroutines (cuBLAS) library provides the high-performance functions for the numerical operations and shows the order of magnitude performance improvements over the other libraries [23,24]. This library also contains various General Matrix-Matrix Multiply (GEMM) routines for the different types of operands (like complex, single data type, double data type etc.)…”

Section: Cublas Accelerated Matrix Multiplication Convolution (Convcamm)mentioning

confidence: 99%

Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs

2017

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Abstract:Convolution is the most computationally intensive task of the Convolutional Neural Network (CNN). It requires a lot of memory storage and computational power. There are different approaches to compute the solution of convolution and reduce its computational complexity. In this paper, a matrix multiplication-based convolution (ConvMM) approach is fully parallelized using concurrent resources of GPU (Graphics Processing Unit) and optimized, considerably improving the performance of the image classifiers and making them applicable to real-time embedded applications. The flow of this CUDA (Compute Unified Device Architecture)-based scheme is optimized using unified memory and hardware-dependent acceleration of matrix multiplication. Proposed flow is evaluated on two different embedded platforms: first on an Nvidia Jetson TX1 embedded board and then on a Tegra K1 GPU of an Nvidia Shield K1 Tablet. The performance of this optimized and accelerated convolutional layer is compared with its sequential and heterogeneous versions. Results show that the proposed scheme significantly improves the overall results including energy efficiency, storage requirement and inference performance. In particular, the proposed scheme on embedded GPUs is hundreds of times faster than the sequential version and delivers tens of times higher performance than the heterogeneous approach.

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Section: Cublas Accelerated Matrix Multiplication Convolution (Convcamm)mentioning

confidence: 99%

Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs

2017

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“…Plaza et al [10] developed three new GPU-based implementations of endmember extraction algorithms: the pixel purity index (PPI), a kernel version of the PPI (KPPI), and the automatic morphological endmember extraction (AMEE) algorithm, and they provided a GPU-based implementation of the fully constrained linear spectral unmixing algorithm. Barberis et al [6] proposed a new parallel implementation of the vertex component analysis (VCA) algorithm for spectral unmixing of remotely sensed hyperspectral data on commodity GPUs. Although there are several parallel implements of the N-FINDR algorithm existing in literature [5,[11][12][13], the speedup of them is less than 30 times, which cannot meet the requirements of real-time applications well.…”

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

Jie

et al. 2013

International Journal of Distributed Sensor Networks

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Hyperspectral imaging sensor becomes increasingly important in multisensor collaborative observation. The spectral mixture problem seriously influences the efficiency of hyperspectral data exploitation, and endmember extraction is one of the key issues. Due to the high computational cost of algorithm and massive quantity of the hyperspectral sensor data, high-performance computing is extremely demanded for those scenarios requiring real-time response. A method of parallel optimization for the wellknown N-FINDR algorithm on graphics processing units (NFINDR-GPU) is proposed to realize fast endmember extraction for massive hyperspectral sensor data in this paper. The implements of the proposed method are described and evaluated using compute unified device architecture (CUDA) based on NVIDA Quadra 600 and Telsa C2050. Experimental results show the effectiveness of NFINDR-GPU. The parallel algorithm is stable for different image sizes, and the average speedup is over thirty times on Telsa C2050, which satisfies the real-time processing requirements.

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“…Thus, it is important to apply high-performance computing technologies to accelerate the hyperspectral image processing algorithms for the time-crucial scenarios [16][17][18]. CPU-GPU heterogeneous parallel mode is a tremendous potential to bridge the gap towards real-time analysis of hyperspectral image [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Literature [20] presents an improved GPU implementation of the PPI algorithm which provides real-time performance. Literature [24] proposes a new parallel implementation of the vertex component analysis (VCA) algorithm for spectral unmixing on commodity GPUs. Literature [36] designs and implements a near real-time automatic target detection algorithm on GPUs for hyperspectral image analysis.…”

Section: Introductionmentioning

confidence: 99%

Optimization of minimum volume constrained hyperspectral image unmixing on CPU–GPU heterogeneous platform

et al. 2014

J Real-Time Image Proc

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Hyperspectral unmixing is essential for efficient hyperspectral image processing. Nonnegative matrix factorization based on minimum volume constraint (MVC-NMF) is one of the most widely used methods for unsupervised unmixing for hyperspectral image without the pure-pixel assumption. But the model of MVC-NMF is unstable, and the traditional solution based on projected gradient algorithm (PG-MVC-NMF) converges slowly with low accuracy. In this paper, a novel parallel method is proposed for minimum volume constrained hyperspectral image unmixing on CPU-GPU Heterogeneous Platform. First, a optimized unmixing model of minimum logarithmic volume regularized NMF is introduced and solved based on the second-order approximation of function and alternating direction method of multipliers (SO-MVC-NMF). Then, the parallel algorithm for optimized MVC-NMF (PO-MVC-NMF) is proposed based on the CPU-GPU heterogeneous platform, taking advantage of the parallel processing capabilities of GPUs and logic control abilities of CPUs. Experimental results based on both simulated and real hyperspectral images indicate that the proposed algorithm is more accurate and robust than the traditional PG-MVC-NMF, and the total speedup of PO-MVC-NMF compared to PG-MVC-NMF is over 50 times.

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Real-Time Implementation of the Vertex Component Analysis Algorithm on GPUs

Cited by 35 publications

References 18 publications

Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs

Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs

Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

Optimization of minimum volume constrained hyperspectral image unmixing on CPU–GPU heterogeneous platform

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