Manufacturing
Manufacturing continues to be one of the most competitive industries globally. Manufacturers are required to innovate across the entire spectrum of product design, analysis, and testing in order to create competitive advantage and grow revenues and profits. AccelerEyes allows engineers, scientists, and analysts at world leading manufacturers to focus on innovation while ArrayFire delivers the performance needed to innovate in a timely manner through higher productivity and increased performance. Regardless of the product being manufactured, AccelerEyes enables the use of GPU technology to increase performance with today's resources all while decreasing the time-to-market.
Example Applications
ArrayFire can help educate students and support research in many application areas including:
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Feature Learning on Images Stanford University |
Speedup: Hours of runtime reduction |
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Feature Learning Architectures with GPU-acceleration
Authors: Andrew Ng, Stanford University Stanford researchers in Andrew Ng's group used GPUs and AccelerEyes software to speed up their work on Feature Learning Architectures. They decided to use AccelerEyes software for this study because of the need to quickly evaluate many architectures on thousands of images. AccelerEyes software taps into the immense computing power of GPUs and speeds up research utilizing many images. Last Updated: 9 Apr 2011 |
Tomography of Vegetation - Filtered Back-Projection and Non-Uniform FFTs Universita di Napoli Federico II |
Speedup: 10X |
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Tomography of Vegetation - Filtered Back-Projection and Non-Uniform FFTs
Authors: Drs. Capozzoli, Curcio, di Vico, and Liseno,
Universita di Napoli Federico II In order to investigate changes of forest biomass, scientists use microwave tomography to image the vegetation. At the smallest scale, individual plants can be imaged to investigate branching and growth, but even synthetic aperture radar can reveal large-scale changes in regional ecology. To the right, you can see the experimental setup to image an individual plant. Last Updated: 16 Aug 2011 |
Action Recognition with Independent Subspace Analysis Stanford University |
Speedup: 4.4X |
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Action Recognition with Independent Subspace Analysis
Authors: Quoc Le, Will Zou, Serena Yeung, Andrew Ng, Stanford University In a paper at this year's CVPR 2011, entitled "Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis", the authors explain how their unsupervised feature learning algorithm competes with other algorithms that are hand crafted or use learned features. For their training purposes, they used a multi-layered stacked convolutional ISA (Independent subspace analysis) network. An ISA is used for learning features from image patches without supervision. Last Updated: 19 Aug 2011 |