Methods and systems for pruning a convolutional neural network (CNN) include calculating a sum of weights for each filter in a layer of the CNN. The filters in the layer are sorted by respective sums of weights. A set of m filters with the smallest sums of weights is filtered to decrease a computational cost of operating the CNN. The pruned CNN is retrained to repair accuracy loss that results from pruning the filters.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for pruning a convolutional neural network (CNN), comprising: calculating a sum of weights for each filter in a layer of the CNN; sorting the filters in the layer by respective sums of weights; pruning m filters with the smallest sums of weights to decrease a computational cost of operating the CNN; and retraining the pruned CNN to repair accuracy loss that results from pruning the filters. 2. The method of claim 1, further comprising pruning feature maps corresponding to the in pruned filters. 3. The method of claim 2, pruning kernels in a subsequent layer that correspond to the pruned feature maps. 4. The method of claim 1, further comprising iterating the steps of pruning and retraining until a threshold CNN accuracy is reached. 5. The method of claim 1, further comprising selecting a number of filters m based on a sensitivity of the layer to pruning. 6. The method of claim 5, wherein a smaller in is selected for layers that have relatively high sensitivities compared to layers that have relatively low sensitivities. 7. The method of claim 5, wherein sensitivity to pruning is measured as a degree of accuracy change. 8. The method of claim 1, wherein retraining the CNN comprises creating a new kernel matrix for the layer. 9. The method of claim 8, wherein retraining the CNN further comprises copying un-pruned kernel weights to the new kernel matrix. 10. A method for pruning a convolutional neural network (CNN), comprising: calculating a sum of weights for each filter in a layer of the CNN; sorting the filters in the layer by respective sums of weights; selecting a number of filters m based on a sensitivity of the layer to pruning, measured as a degree of accuracy change; pruning m filters with the smallest sums of weights to decrease a computational cost of operating the CNN; pruning feature maps corresponding to the m pruned filters; pruning kernels in a subsequent layer that correspond to the pruned feature maps; and retraining the pruned CNN to repair accuracy loss that results from pruning the filters. 11. A system for pruning a convolutional neural network (CNN), comprising: a pruning module comprising a processor configured to calculate a sum of weights for each filter in a layer of the CNN, to sort the filters in the layer by respective sums of weights, and to prune in filters with the smallest sums of weights to decrease a computational cost of operating the CNN; and a training module configured to retrain the pruned CNN to repair accuracy loss that results from pruning the filters. 12. The system of claim 11, wherein the pruning module is further configured to prune feature maps corresponding to the m pruned filters. 13. The system of claim 12, wherein the pruning module is further configured to prune kernels in a subsequent layer that correspond to the pruned feature maps. 14. The system of claim 11, wherein the pruning module and the training module are further configured to iterate the steps of pruning and retraining until a threshold CNN accuracy is reached. 15. The system of claim 11, wherein the pruning module is further configured to select a number of filters m based on a sensitivity of the layer to pruning. 16. The system of claim 15, wherein a smaller in is selected for layers that have relatively high sensitivities compared to layers that have relatively low sensitivities. 17. The system of claim 15, wherein sensitivity to pruning is measured as a degree of accuracy change. 18. The system of claim 11, wherein the training module is further configured to create a new kernel matrix for the layer. 19. The system of claim 11, wherein the training module is further configured to copy un-pruned kernel weights to the new kernel matrix.