Structured Directional Pruning via Perturbation Orthogonal Projection

Despite the great potential of artificial intelligence (AI), which promotes machines to mimic human intelligence in performing tasks, it requires a deep/extensive model with a sufficient number of parameters to enhance the expressive ability. This aspect often hinders the application of AI on resource-constrained devices. Structured pruning is an effective compression technique that reduces the computation of neural networks. However, it typically achieves parameter reduction at the cost of non-negligible accuracy loss, necessitating fine-tuning. This paper introduces a novel technique called Structured Directional Pruning (SDP) and its fast solver, Alternating Structured Directional Pruning ( AltSDP ). SDP is a general energy-efficient coarse-grained pruning method that enables efficient model pruning without requiring fine-tuning or expert knowledge of the desired sparsity level. Theoretical analysis confirms that the fast solver, AltSDP , achieves SDP asymptotically after sufficient training. Experimental results validate that AltSDP reaches the same minimum valley as the vanilla optimizer, namely stochastic gradient descent (SGD), while maintaining a constant training loss. Additionally, AltSDP achieves state-of-the-art pruned accuracy integrating pruning into the initial training process without the need for fine-tuning. Consequently, the newly proposed SDP, along with its fast solver AltSDP , can significantly facilitate the development of shrinking deep neural networks (DNNs) and enable the deployment of AI on resource-constrained devices.