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The excessive computational requirements of modern artificial neural networks(ANNs) are posing limitations on the machines that can run them. Sparsificationof ANNs is often motivated by time, memory and energy savings only during modelinference, yielding no benefits during training. A growing body of work is nowfocusing on providing the benefits of model sparsification also duringtraining. While these methods greatly improve the training efficiency, thetraining algorithms yielding the most accurate models still materialize thedense weights, or compute dense gradients during training. We propose anefficient, always-sparse training algorithm with excellent scaling to largerand sparser models, supported by its linear time complexity with respect to themodel width during training and inference. Moreover, our guided stochasticexploration algorithm improves over the accuracy of previous sparse trainingmethods. We evaluate our method on CIFAR-10/100 and ImageNet using ResNet, VGG,and ViT models, and compare it against a range of sparsification methods.

Always-Sparse Training by Growing Connections with Guided Stochastic Exploration