Principal Investigator Song Han
Winograd’s minimal filtering algorithm and network pruning both reduce the operations in CNNs. Unfortunately, these two methods cannot be combined. We propose two modifications to Winograd-based CNNs to enable these methods to exploit sparsity. First, we prune the weights in the ”Winograd domain” to exploit static weight sparsity. Second, we move the ReLU operation into the ”Winograd domain” to improve the sparsity of the transformed activations. On CIFAR-10, our method reduces the number of multiplications in the VGG-nagadomi model by 10.2× with no loss of accuracy.