Deep learning with a GPU that supports fp16

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NVDIA has released the new RTX 2XXX and 3XXX series that support fp16 that accelrates training process.
Does Matlab support this?
Thank you
Krishna Bindumadhavan
Krishna Bindumadhavan on 14 Sep 2019
There is support for half precision in MATLAB via the half precision object, available in the fixed point designer toolbox:
General Code generation support for half precision data type via MATLAB Coder and GPU Coder is under active development. This functionality is expected in an upcoming release.
As mentioned below, there is no support currently for using half for training a deep learning network in MATLAB. This is expected in a future release.

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Accepted Answer

Joss Knight
Joss Knight on 29 Aug 2019
You can take advantage of FP16 when generating code for prediction on a deep neural network. Follow the pattern of the Deep Learning Prediction with NVIDIA TensorRT example but set the DataType property of the DeepLearningConfig to 'fp16'. This will use the Tensor cores on a Volta or Turing card such as the RTX series.
There is no way yet to use half precision or Tensor cores for training a deep neural network in MATLAB. This is expected in an upcoming release.
Joss Knight
Joss Knight on 24 Feb 2021
You can use the Deep Network Quantizer to calibrate a trained network for 8-bit reduced precision types. For now, fp16 is not supported, and quantization-aware training is not supported.
With an Ampere card, using the latest R2021a release of MATLAB (soon to be released), you will be able to take advantage of the Tensor cores using single precision because of the new TF32 datatype that cuDNN leverages when performing convolutions on an Ampere card.

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