Today on the podcast, we speak with Ian Buck and Kari Briski of NVIDIA about new updates and achievements in deep learning. Ian begins by telling hosts Jon and Mark about his first project at NVIDIA, CUDA, and how it has helped expand and pave the way for future projects in super computing, AI, and gaming. CUDA is used extensively in computer vision, speech and audio applications, and machine comprehension, Kari elaborates.

NVIDIA recently announced their new Tensor Cores, which maximize their GPUs and make it easier for users to achieve peak performance. Working with the Tensor Cores, TensorFlow AMP is an acceleration into the TensorFlow Framework. It automatically makes the right choices for neural networks and maximizes performance, while still maintaining accuracy, with only a two line change in Tensor Flow script.

Just last year, NVIDIA announced their T4 GPU with Google Cloud Platform. This product is designed for inferences, the other side of AI. Because AI is becoming so advanced, complicated, and fast, the GPUs on the inference side have to be able to handle the workload and produce inferences just as quickly. T4 and Google Cloud accomplish this together. Along with T4, NVIDIA has introduced TensorRT, a software framework for AI inference that’s integrated into TensorFlow.