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:

Feature Learning on Images Stanford University

Speedup: Hours of runtime reduction

Feature Learning Architectures with GPU-acceleration Authors: Andrew Ng, Stanford University Speedup: Ability to process many images in parallel 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. Learn More Last Updated: 9 Apr 2011

Tomography of Vegetation - Filtered Back-Projection and Non-Uniform FFTs Universita di Napoli Federico II

Speedup: 10X

Tomography of Vegetation - Filtered Back-Projection and Non-Uniform FFTs Authors: Drs. Capozzoli, Curcio, di Vico, and Liseno, Universita di Napoli Federico II Speedup: 10X 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. Learn More Last Updated: 16 Aug 2011

Action Recognition with Independent Subspace Analysis Stanford University

Speedup: 4.4X

Action Recognition with Independent Subspace Analysis Authors: Quoc Le, Will Zou, Serena Yeung, Andrew Ng, Stanford University Speedup: 4.4X 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. Learn More Last Updated: 19 Aug 2011