Parallelization of cellular neural networks on GPU

Ho, Tze-Yui; Lam, Ping-Man; Leung, Chi-Sing

doi:10.1016/j.patcog.2008.01.018

Cited by 49 publications

(20 citation statements)

References 13 publications

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“…While relatively modest in size, these images illuminate the shortcomings of CPU-based CNN simulation and image processing. The results in Table II illustrate two significant points: first, that CNN simulation on CPUs is indeed problematic, even at modest image sizes; CNNs have been implemented using GPUs in the recent past using shaders to modify the GPU's rendering pipeline [7]. This is a significantly less convenient approach, requiring the programmer to formulate the algorithm in terms of pixels, textures, vertexes, and other graphics primitives.…”

Section: Iy Resultsmentioning

confidence: 99%

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GPU-based simulation of cellular neural networks for image processing

Dolan

DeSouza

2009

2009 International Joint Conference on Neural Networks

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improvement translates to faster image processing algorithms compared to traditional CPU-based algorithms. topology uniform 2D grid usually feed-forward processing element dynamic equations nonlinear weighted sum common uses image processing classification, control B. CNN TopologyCNNs are composed of many cells arranged in a grid, M.To simplify discussion, we will assume these grids are always square with dimensions m x m for m 2 cells. Each cell in the grid is denoted Vij for i, j EM. Thus each cell is labeled from VII to V m m .

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Section: Iy Resultsmentioning

confidence: 99%

“…Simple GPU-based CNN simulations have been demonstrated that run much faster than CPU-based CNN simulations [7], [6]. The research presented here examines whether this Abstract-The inherent massive parallelism of cellular neural networks makes them an ideal computational platform for kernelbased algorithms and image processing.…”

Section: Anns Cnnsmentioning

confidence: 99%

GPU-based simulation of cellular neural networks for image processing

Dolan

DeSouza

2009

2009 International Joint Conference on Neural Networks

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“…On one hand, NNs are very flexible and can be trained to mimic the behavior of any physical system with relative ease of implementation. On the other hand, a major drawback of NNs is their potentially long training time (several hours or even days) because they are very computation and dataintensive [18]. Nevertheless, their capacity to execute a large number of operations simultaneously and with relatively low data transfer makes them a potentially attractive concept for GPU computing [19].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Thermal Simulation of Integrated Circuits on GPUs

Sridhar

Vincenzi

Ruggiero

et al. 2012

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-With the rising challenges in heat removal in integrated circuits (ICs), the development of thermal-aware computing architectures and run-time management systems has become indispensable to the continuation of IC design scaling. These thermal-aware design technologies of the future strongly depend on the availability of efficient and accurate means for thermal modeling and analysis. These thermal models must have not only the sufficient accuracy to capture the complex mechanisms that regulate thermal diffusion in ICs, but also a level of abstraction that allows for their fast execution for design space exploration. In this paper, we propose an innovative thermal modeling approach for full-chips that can handle the scalability problem of transient heat flow simulation in large 2-D/3-D multiprocessor ICs. This is achieved by parallelizing the computation-intensive task of transient temperature tracking using neural networks and exploiting the computational power of massively parallel graphics processing units. Our results show up to 35× run-time speedup compared to state-of-the-art IC thermal simulation tools while keeping the error lower than 1°C. Speedups scale with the size of the 3-D multiprocessor ICs and our proposed method serves as a valuable design space exploration tool.

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“…In particular, a fully automated segmentation algorithm, based on the CNNs (Chua andYang, 1988a, 1988b), is implemented in this paper as an integrated part of the proposed framework. The proposed algorithms, which can be applied on CNN-UM (Chua and Roska, 1992), offer a view of parallel computation, which remains reasonably efficient even when applied on Graphics Processing Unit (GPU) simulators (Dolan and DeSouza, 2009;Ho et al, 2008;Soos et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

A multi-view approach to cDNA micro-array analysis

Zineddin

Wang

Shi

et al. 2010

IJCBDD

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Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.Keywords: micro-array image processing; ITE; image transformation engine; median filter; top-hat filter; linear complex diffusion; adaptive segmentation; computational biology; drug design.Reference to this paper should be made as follows: Zineddin, B., Wang, Z., Shi, Y., Li, Y., Du, M. and Liu, X. (2010) 'A multi-view approach to cDNA micro-array analysis', Int.

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Parallelization of cellular neural networks on GPU

Cited by 49 publications

References 13 publications

GPU-based simulation of cellular neural networks for image processing

GPU-based simulation of cellular neural networks for image processing

Neural Network-Based Thermal Simulation of Integrated Circuits on GPUs

A multi-view approach to cDNA micro-array analysis

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