Efficient Sparse Artificial Neural Networks

TL;DR

This paper introduces two evolutionary methods for training sparse artificial neural networks, inspired by the brain's structure, resulting in models that are more accurate, efficient, and require fewer parameters.

Contribution

The paper proposes novel evolutionary techniques that adapt both the structure and parameters of ANNs during training, enhancing performance and efficiency over existing methods.

Findings

Achieved 40% fewer parameters in ResNet47 with 12% higher accuracy.

Converged 7 times faster on CIFAR10 with 6% accuracy improvement.

Improved generalization and reduced training sample requirements.

Abstract

The brain, as the source of inspiration for Artificial Neural Networks (ANN), is based on a sparse structure. This sparse structure helps the brain to consume less energy, learn easier and generalize patterns better than any other ANN. In this paper, two evolutionary methods for adopting sparsity to ANNs are proposed. In the proposed methods, the sparse structure of a network as well as the values of its parameters are trained and updated during the learning process. The simulation results show that these two methods have better accuracy and faster convergence while they need fewer training samples compared to their sparse and non-sparse counterparts. Furthermore, the proposed methods significantly improve the generalization power and reduce the number of parameters. For example, the sparsification of the ResNet47 network by exploiting our proposed methods for the image classification of ImageNet dataset uses 40 % fewer parameters while the top-1 accuracy of the model improves by 12% and 5% compared to the dense network and their sparse counterpart, respectively. As another example, the proposed methods for the CIFAR10 dataset converge to their final structure 7 times faster than its sparse counterpart, while the final accuracy increases by 6%.