Newsletter

Newsletter
 

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:

  • Modeling and Simulation
  • Finite Element Analysis
  • Image Processing
  • Signal Processing
  • Fluid Flow Analysis
  • Math and Statistics
  • Optimization
  • And much more...

Feature Learning on Images
Stanford University
Speedup: Hours of runtime reduction


Feature Learning

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.

Last Updated: 9 Apr 2011

Tomography of Vegetation - Filtered Back-Projection and Non-Uniform FFTs
Universita di Napoli Federico II
Speedup: 10X


Filtered Back-Projection

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.

Last Updated: 16 Aug 2011

Action Recognition with Independent Subspace Analysis
Stanford University
Speedup: 4.4X


Feature Learning

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.

Last Updated: 19 Aug 2011