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Recent years have seen a growing adoption of Transformer models such as BERTin Natural Language Processing and even in Computer Vision. However, due totheir size, there has been limited adoption of such models withinresource-constrained computing environments. This paper proposes novel pruningalgorithm to compress transformer models by eliminating redundant AttentionHeads. We apply the A* search algorithm to obtain a pruned model with strictaccuracy guarantees. Our results indicate that the method could eliminate asmuch as 40% of the attention heads in the BERT transformer model with no lossin accuracy.

Pruning Attention Heads of Transformer Models Using A* Search: A Novel Approach to Compress Big NLP Architectures