Pruning Deep Neural Networks for Efficient Inference and Training

Deep neural networks are powerful tools for machine learning, but they can be computationally expensive to train and deploy. One way to address this issue is by pruning away unimportant weights and connections in the network. However, directly removing weights without considering their importance can lead to a loss of accuracy. This article introduces a novel approach called "sparsity-aware adaptive magnitude pruning" (SAAM), which dynamically adjusts the level of sparsity based on the importance of each weight.
The SAAM method is designed for unstructured, semi-structured, and structured sparsity scenarios. In unstructured sparsity, the weights are randomly removed without any further fine-tuning or iterative procedures. For semi-structured and structured sparsity, the importance of each weight is first computed using a score-based method, such as magnitude pruning or loss gradient descent. Then, the weights with the highest scores are kept, while the others are set to zero.
The SAAM method has several advantages over traditional pruning methods. Firstly, it adaptively adjusts the level of sparsity based on the importance of each weight, which leads to better accuracy compared to fixed pruning levels. Secondly, it does not require any additional computations or iterative procedures, making it computationally efficient. Finally, the SAAM method can be applied to various deep learning architectures and frameworks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
In summary, SAAM is a novel pruning method that dynamically adjusts the level of sparsity based on the importance of each weight. It leads to better accuracy compared to traditional fixed pruning methods and is computationally efficient. The SAAM method can be applied to various deep learning architectures and frameworks, making it a versatile tool for improving the efficiency of deep neural networks.