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PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination

Saurabh Goyal · Anamitra Roy Choudhury · Saurabh Raje · Venkatesan Chakaravarthy · Yogish Sabharwal · Ashish Verma

Keywords: [ Applications - Language, Speech and Dialog ] [ Other ] [ Natural Language Processing / Dialogue ] [ Sparsity and Compressed Sensing ] [ Deep Sequence Models ]


We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pertaining to word-vectors (intermediate encoder outputs) and eliminating the redundant vectors. b) determining which word-vectors to eliminate by developing a strategy for measuring their significance, based on the self-attention mechanism. c) learning how many word-vectors to eliminate by augmenting the BERT model and the loss function. Experiments on the standard GLUE benchmark shows that PoWER-BERT achieves up to 4.5x reduction in inference time over BERT with < 1% loss in accuracy. We show that PoWER-BERT offers significantly better trade-off between accuracy and inference time compared to prior methods. We demonstrate that our method attains up to 6.8x reduction in inference time with < 1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT. The code for PoWER-BERT is publicly available at

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