Subspace Node Pruning

A significant increase in the commercial use of deep neural network models increases the need for efficient AI. Node pruning is the art of removing computational units such as neurons, filters, attention heads, or even entire layers while keeping network performance at a maximum. This can significantly reduce the inference time of a deep network and thus enhance its efficiency. Few of the previous works have exploited the ability to recover performance by reorganizing network parameters while pruning. In this work, we propose to create a subspace from unit activations which enables node pruning while recovering maximum accuracy. We identify that for effective node pruning, a subspace can be created using a triangular transformation matrix, which we show to be equivalent to Gram-Schmidt orthogonalization, which automates this procedure. We further improve this method by reorganizing the network prior to subspace formation. Finally, we leverage the orthogonal subspaces to identify layer-wise pruning ratios appropriate to retain a significant amount of the layer-wise information. We show that this measure outperforms existing pruning methods on VGG networks. We further show that our method can be extended to other network architectures such as residual networks.

Efficient Deep Learning with Decorrelated Backpropagation

The backpropagation algorithm remains the dominant and most successful method for training deep neural networks (DNNs). At the same time, training DNNs at scale comes at a significant computational cost and therefore a high carbon footprint. Converging evidence suggests that input decorrelation may speed up deep learning. However, to date, this has not yet translated into substantial improvements in training efficiency in large-scale DNNs. This is mainly caused by the challenge of enforcing fast and stable network-wide decorrelation. Here, we show for the first time that much more efficient training of very deep neural networks using decorrelated backpropagation is feasible. To achieve this goal we made use of a novel algorithm which induces network-wide input decorrelation using minimal computational overhead. By combining this algorithm with careful optimizations, we obtain a more than two-fold speed-up and higher test accuracy compared to backpropagation when training a 18-layer deep residual network. This demonstrates that decorrelation provides exciting prospects for efficient deep learning at scale.