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This paper studies structured sparse training of CNNs with a gradual pruningtechnique that leads to fixed, sparse weight matrices after a set number ofepochs. We simplify the structure of the enforced sparsity so that it reducesoverhead caused by regularization. The proposed training methodology Campfireexplores pruning at granularities within a convolutional kernel and filter.  We study various tradeoffs with respect to pruning duration, level ofsparsity, and learning rate configuration. We show that our method creates asparse version of ResNet-50 and ResNet-50 v1.5 on full ImageNet while remainingwithin a negligible <1% margin of accuracy loss. To ensure that this type ofsparse training does not harm the robustness of the network, we alsodemonstrate how the network behaves in the presence of adversarial attacks. Ourresults show that with 70% target sparsity, over 75% top-1 accuracy isachievable.

Campfire: Compressible, Regularization-Free, Structured Sparse Training for Hardware Accelerators