Cellular neural networks and visual computing: foundations and applications
Cellular neural networks and visual computing: foundations and applications
Cg: a system for programming graphics hardware in a C-like language
ACM SIGGRAPH 2003 Papers
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
GPU-Based Real-time Simulation and Rendering of Unbounded Ocean Surface
CAD-CG '05 Proceedings of the Ninth International Conference on Computer Aided Design and Computer Graphics
Discrete Wavelet Transform on Consumer-Level Graphics Hardware
IEEE Transactions on Multimedia
An RBF-based compression method for image-based relighting
IEEE Transactions on Image Processing
Adaptative Resonance Theory Fuzzy Networks Parallel Computation Using CUDA
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
GPU-based simulation of cellular neural networks for image processing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A novel approach for distributed application scheduling based on prediction of communication events
Future Generation Computer Systems
Fuzzy ARTMAP based neural networks on the GPU for high-performance pattern recognition
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Fast thermal simulation of 2D/3D integrated circuits exploiting neural networks and GPUs
Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design
Neural PCA and maximum likelihood hebbian learning on the GPU
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Computer Methods and Programs in Biomedicine
Fast fingerprint identification for large databases
Pattern Recognition
| Hi-index | 0.01 |
Recently, cellular neural networks (CNNs) have been demonstrated to be a highly effective paradigm applicable in a wide range of areas. Typically, CNNs can be implemented using VLSI circuits, but this would unavoidably require additional hardware. On the other hand, we can also implement CNNs purely by software; this, however, would result in very low performance when given a large CNN problem size. Nowadays, conventional desktop computers are usually equipped with programmable graphics processing units (GPUs) that can support parallel data processing. This paper introduces a GPU-based CNN simulator. In detail, we carefully organize the CNN data as 4-channel textures, and efficiently implement the CNN computation as fragment programs running in parallel on a GPU. In this way, we can create a high performance but low-cost CNN simulator. Experimentally, we demonstrate that the resultant GPU-based CNN simulator can run 8-17 times faster than a CPU-based CNN simulator.