# Efficient Sparse-Winograd Convolutional Neural Networks Source: [Efficient Sparse-Winograd Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:56:11.088Z Authors: Xingyu Liu, Jeff Pool, Song Han, William J. Dally Published: 2018-02-18T12:29:05.000Z ## Abstract Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed to perform the convolutions. Winograd's minimal filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can reduce the operation count, but these two methods cannot be directly combined $-$ applying the Winograd transform fills in the sparsity in both the weights and the activations. We propose two modifications to Winograd-based CNNs to enable these methods to exploit sparsity. First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations. Second, we prune the weights in the Winograd domain to exploit static weight sparsity. For models on CIFAR-10, CIFAR-100 and ImageNet datasets, our method reduces the number of multiplications by $10.4\\times$, $6.8\\times$ and $10.8\\times$ respectively with loss of accuracy less than $0.1\\%$, outperforming previous baselines by $2.0\\times$-$3.0\\times$. We also show that moving ReLU to the Winograd domain allows more aggressive pruning. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()