Efficient Sparse-Winograd Convolutional Neural NetworksDownload PDF

15 Feb 2018 (modified: 16 Jun 2024)ICLR 2018 Conference Blind SubmissionReaders: Everyone
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 straightforwardly 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 10.4x, 6.8x and 10.8x respectively with loss of accuracy less than 0.1%, outperforming previous baselines by 2.0x-3.0x. We also show that moving ReLU to the Winograd domain allows more aggressive pruning.
TL;DR: Prune and ReLU in Winograd domain for efficient convolutional neural network
Keywords: deep learning, convolutional neural network, pruning
Code: [![github](/images/github_icon.svg) xingyul/Sparse-Winograd-CNN](https://github.com/xingyul/Sparse-Winograd-CNN)
Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [CIFAR-100](https://paperswithcode.com/dataset/cifar-100)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:1802.06367/code)
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