Single precision (also known as 32-bit) is a common floating point format ( float in C-derived programming languages), and 64-bit, known as double precision ( double).ĭeep Neural Networks (DNNs) have led to breakthroughs in a number of areas, including:ĭNN complexity has been increasing to achieve these results, which in turn has increased the computational resources required to train these networks. Half precision (also known as FP16) data compared to higher precision FP32 vs FP64 reduces memory usage of the neural network, allowing training and deployment of larger networks, and FP16 data transfers take less time than FP32 or FP64 transfers. Mixed precision is the combined use of different numerical precisions in a computational method. The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK. Adding loss scaling to preserve small gradient values.Porting the model to use the FP16 data type where appropriate.

Using mixed precision training requires two steps: Since the introduction of Tensor Cores in the Volta and Turing architectures, significant training speedups are experienced by switching to mixed precision - up to 3x overall speedup on the most arithmetically intense model architectures. Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network.

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