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Placing text labels on maps and diagrams
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Mapping combinatorial optimization problems onto neural networks
Information Sciences—Intelligent Systems: An International Journal
An empirical study of algorithms for point-feature label placement
ACM Transactions on Graphics (TOG)
Numerical Recipes in C++: the art of scientific computing
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INFORMS Journal on Computing
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Proceedings of the 35th annual ACM/IEEE international symposium on Microarchitecture
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Proceedings of the Eurographic workshop on Computer animation and simulation
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Nonlinear optimization framework for image-based modeling on programmable graphics hardware
ACM SIGGRAPH 2003 Papers
Real-time cloud simulation and rendering
Real-time cloud simulation and rendering
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SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Understanding the efficiency of GPU algorithms for matrix-matrix multiplication
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Limitations of neural networks for solving traveling salesman problems
IEEE Transactions on Neural Networks
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This paper discusses the application of parallel Hopfield neural networks in solving the Point-Feature labeling placement (PFLP) problem by using programmable graphics hardware found in a commodity PC. In this paper, we focus on two aspects. The first aspect concerns mapping the PFLP onto parallel Hopfield neural network. The second aspect is the detailed method of implementing the parallel Hopfield neural network on graphics hardware. We demonstrate the effectiveness of implementing the parallel Hopfield network by solving the PFLP problem. Moreover, our proposal makes use of the advantages of the parallel Hopfield network on low-cost platforms.