Sub-graph in frequency domain and dynamic selection of convolution implementation on a GPU

A method, comprising:

• obtaining a sub-graph of a convolutional neural network to be executed at least in part in a frequency domain;
• generating a predicted level of activation sparsity for one or more layers of the convolutional neural network;
• and
• applying one or more convolutional computations in the sub-graph in the frequency domain, wherein the one or more convolutional computations are performed at variable levels of integer precisions based at least in part on the predicted level of activation sparsity of a layer in the convolutional neural network using a first set of execution resources, while internal computations are performed in a baseline precision level using a second set of execution resources, different from the first set of execution resources.

One or more non-transitory computer readable media comprising instructions which, when executed by processing circuitry, configure the processing circuitry to:

• obtain a sub-graph of a convolutional neural network to be executed at least in part in a frequency domain;
• generate a predicted level of activation sparsity for one or more layers of the convolutional neural network;
• and
• apply one or more convolutional computations in the sub-graph in the frequency domain, wherein the one or more convolutional computations are performed at variable levels of integer precisions based at least in part on the predicted level of activation sparsity of a layer in the convolutional neural network using a first set of execution resources, while internal computations are performed in a baseline precision level using a second set of execution resources, different from the first set of execution resources.