Patent ID: 11887001
Assignee: INTEL CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 7:
8. A method comprising:
performing layer-wise pruning a specified set of parameters from each layer of a reference neural network model in accordance with a Joint Feed-forward and Backward Propagation Approximation (JFBPA) to generate a second neural network model having a higher sparsity rate than the reference neural network model, wherein a layer in the second neural network model is computed using the JFBPA based on a number of zero parameters in a corresponding matrix;
retraining the second neural network model in accordance with a set of training data to generate a retrained second neural network model; and
designating the retrained second neural network model as a final neural network model if a specified target sparsity rate or a specified number of alternating pruning and retraining iterations has been reached, and performing additional layer-wise pruning of the retrained second neural network model if both the specified target sparsity rate and the specified number of alternating pruning and retraining iterations have not been reached.