DEEP LEARNING PLATFORMS & GPUS: AN INTERVIEW WITH BRYAN CATANZARO

High-performance graphics cards, typically associated with gaming, have become popular over the last few years in an area many might not expect: artificial intelligence. Many experts attribute recent acceleration of success in AI to a wider availability and use of graphics processing units (GPUs), as their advantages include cores designed for running multiple tasks simultaneously, which can efficiently handle the vector and matrix operations that are prevalent in deep learning. Training and deploying state of the art deep neural networks is very computationally intensive, and, while modern GPUs offer high density computation, researchers need more than a fast processor -- they also need optimized libraries, and tools to efficiently program so that they can experiment with new ideas. Bryan Catanzaro, VP of Applied Deep Learning Research at NVIDIA, joined us at the 2017 Deep Learning Summit in San Francisco, to share expertise on GPUs and platforms for deep learning, as well as giving insights on the latest deep learning developments at NVIDIA. I asked him some questions at the summit to learn more about his work. What motivated you to begin your work in deep learning?