This post is part of a series about optimizing end-to-end AI. The performance of AI models is heavily influenced by the precision of the computational resources being used. Lower precision can lead to faster processing speeds and reduced memory usage, while higher precision can contribute to more accurate results. Finding the right balance between precision and performance is crucial for��
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Object detection remains the primary driver for applications such as autonomous driving and intelligent video analytics. Object detection applications require substantial training using vast datasets to achieve high levels of accuracy. NVIDIA GPUs excel at the parallel compute performance required to train large networks in order to generate datasets for object detection inference.
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Our most popular question is ��What can I do to get great GPU performance for deep learning?�� We��ve recently published a detailed Deep Learning Performance Guide to help answer this question. The guide explains how GPUs process data and gives tips on how to design networks for better performance. We also take a close look at Tensor Core optimization to help improve performance. This post takes a��
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Machine learning harnesses computing power to solve a variety of ��hard�� problems that seemed impossible to program using traditional languages and techniques.?Machine learning?avoids?the need for a programmer to explicitly program the steps in solving a complex pattern-matching problem such as understanding speech or recognizing objects within an image. NVIDIA aims to bring machine learning to��
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Neural networks with thousands of layers and millions of neurons demand high performance and faster training times. The complexity and size of neural networks continue to grow. Mixed-precision training using Tensor Cores on Volta and Turing architectures enable higher performance while maintaining network accuracy for heavily compute- and memory-intensive Deep Neural Networks (DNNs).
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Double-precision floating point (FP64) has been the de facto standard for doing scientific simulation for several decades. Most numerical methods used in engineering and scientific applications require the extra precision to compute correct answers or even reach an answer. However, FP64 also requires more computing resources and runtime to deliver the increased precision levels.
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Neural network models have quickly taken advantage of NVIDIA Tensor Cores for deep learning since their introduction in the Tesla V100 GPU last year. For example, new performance records for ResNet50 training were announced recently with Tensor Core-based solutions. (See the NVIDIA developer post on new performance milestones for additional details). NVIDIA��s cuDNN library enables CUDA��
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A defining feature of the new NVIDIA Volta GPU architecture is Tensor Cores, which give the NVIDIA V100 accelerator a peak throughput that is 12x the 32-bit floating point throughput of the previous-generation NVIDIA P100. Tensor Cores enable you to use mixed-precision for higher throughput without sacrificing accuracy. Tensor Cores provide a huge boost to convolutions and matrix operations.
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Deep Neural Networks (DNNs) have lead to breakthroughs in a number of areas, including image processing and understanding, language modeling, language translation, speech processing, game playing, and many others. DNN complexity has been increasing to achieve these results, which in turn has increased the computational resources required to train these networks. Mixed-precision training lowers the��
]]>Update, March 25, 2019: The latest Volta and Turing GPUs now incoporate Tensor Cores, which accelerate certain types of FP16 matrix math. This enables faster and easier mixed-precision computation within popular AI frameworks. Making use of Tensor Cores requires using CUDA 9 or later. NVIDIA has also added automatic mixed precision capabilities to TensorFlow, PyTorch, and MXNet.
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