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Humans read and write hundreds of billions of messages every day. Further,due to the availability of large datasets, large computing systems, and betterneural network models, natural language processing (NLP) technology has madesignificant strides in understanding, proofreading, and organizing thesemessages. Thus, there is a significant opportunity to deploy NLP in myriadapplications to help web users, social networks, and businesses. In particular,we consider smartphones and other mobile devices as crucial platforms fordeploying NLP models at scale. However, today's highly-accurate NLP neuralnetwork models such as BERT and RoBERTa are extremely computationallyexpensive, with BERT-base taking 1.7 seconds to classify a text snippet on aPixel 3 smartphone. In this work, we observe that methods such as groupedconvolutions have yielded significant speedups for computer vision networks,but many of these techniques have not been adopted by NLP neural networkdesigners. We demonstrate how to replace several operations in self-attentionlayers with grouped convolutions, and we use this technique in a novel networkarchitecture called SqueezeBERT, which runs 4.3x faster than BERT-base on thePixel 3 while achieving competitive accuracy on the GLUE test set. TheSqueezeBERT code will be released.

SqueezeBERT: What can computer vision teach NLP about efficient neural networks?