Pruning (artificial neural network)
In the context of artificial neural network, pruning is the practice of removing parameters (which may entail removing individual parameters, or parameters in groups such as by neurons) from an existing network.[1] The goal of this process is to maintain accuracy of the network while increasing its efficiency. This can be done to reduce the computational resources required to run the neural network. A process of pruning takes place in the brain of mammals during development [2].
Node (neuron) pruning
A basic algorithm for pruning is as follows:[3][4]
- Evaluate the importance of each neuron.
- Rank the neurons according to their importance (assuming there is a clearly defined measure for "importance").
- Remove the least important neuron.
- Check a termination condition (to be determined by the user) to see whether to continue pruning.
Recently a highly pruned three layer tree architecture, has achieved a similar success rate to that of LeNet-5 on the CIFAR-10 dataset with a lesser computational complexity.[5]
Edge (weight) pruning
Most work on neural network pruning focuses on removing weights, namely, setting their values to zero. Early work suggested to also change the values of non-pruned weights [6].
References
- ^ Blalock, Davis; Ortiz, Jose Javier Gonzalez; Frankle, Jonathan; Guttag, John (2020-03-06). "What is the State of Neural Network Pruning?". arXiv:2003.03033 [cs.LG].
- ^ Chechik, Gal; Meilijson, Isaac; Ruppin, Eytan (1998-10). "Synaptic Pruning in Development: A Computational Account". Neural Computation. 10 (7): 1759–1777. doi:10.1162/089976698300017124. ISSN 0899-7667.
{{cite journal}}: Check date values in:|date=(help) - ^ Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2016). Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440.
- ^ Pruning deep neural networks to make them fast and small.
- ^ Meir, Yuval; Ben-Noam, Itamar; Tzach, Yarden; Hodassman, Shiri; Kanter, Ido (2023-01-30). "Learning on tree architectures outperforms a convolutional feedforward network". Scientific Reports. 13 (1): 962. doi:10.1038/s41598-023-27986-6. ISSN 2045-2322. PMC 9886946. PMID 36717568.
- ^ Chechik, Gal; Meilijson, Isaac; Ruppin, Eytan (2001-04). "Effective Neuronal Learning with Ineffective Hebbian Learning Rules". Neural Computation. 13 (4): 817–840. doi:10.1162/089976601300014367. ISSN 0899-7667.
{{cite journal}}: Check date values in:|date=(help)
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