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