Went from training 700 img/s in MNIST to 1500 img/s (using CUDA) to 4000 img/s (using cuDNN) that is just freaking amazing! @GPUComputing
— Leon Palafox (@leonpalafox) March 27, 2015
I stumbled upon the above tweet by Leon Palafox, a Postdoctoral Fellow at the The University of Arizona Lunar and Planetary Laboratory, and reached out to him to discuss his success with GPUs and share it with other developers interested in using deep learning for image processing.
Tell us about your research at The University of Arizona
We are working on developing a tool that can automatically identify various geological processes on the surface of Mars. Examples of geological processes include impact cratering and volcanic activity; however, these processes can generate landforms that look very similar, even though they form via vastly different mechanisms. For example, small impact craters and volcanic craters can be easily confused because they can both exhibit a prominent rim surrounding a central topographic depression.
Of particular interest to our research group is the automated mapping of volcanic rootless cones as Figure 2 shows. These landforms are generated by explosive interactions between lava and ground ice, and therefore mapping the global distribution of rootless cones on Mars would contribute to a better understanding of the distribution of near-surface water on the planet. However, to do this we must first develop algorithms that can correctly distinguish between landforms of similar appearance. This is a difficult task for planetary geologists, but we are already having great success by applying state-of-the-art artificial neural networks to data acquired by the High Resolution Imaging Science Experiment (HiRISE) camera, which is onboard the Mars Reconnaissance Orbiter (MRO) satellite.