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We propose an adaptive projection-gradient descent-shrinkage-splitting method(APGDSSM) to integrate penalty based channel pruning into quantization-awaretraining (QAT). APGDSSM concurrently searches weights in both the quantizedsubspace and the sparse subspace. APGDSSM uses shrinkage operator and asplitting technique to create sparse weights, as well as the Group Lassopenalty to push the weight sparsity into channel sparsity. In addition, wepropose a novel complementary transformed l1 penalty to stabilize the trainingfor extreme compression.

Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method