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Description

We examine the acceleration of a robotic arm calibration algorithm using a general purpose GPU (GPGPU). The algorithm utilized requires a radial basis function neural network for calibration and takes approximately 9 days to run on a standard desktop computer. The most time consuming component of this algorithm is a matrix inversion operation. This is carried out on an NVIDIA GPGPU using the Cholesky Factorization. On an NVIDIA Tesla S1070 GPGPU, this same algorithm ran about 300 times faster than a standard desktop computer running an optimized version of the code.

Publication Date

4-18-2012

Project Designation

Graduate Research

Primary Advisor

Tarek M. Taha

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium poster

Accelerating Robotic Arm Calibration on GPGPUs

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