SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
This work addresses the need for efficient NLP deployment on mobile devices, offering a significant speedup for applications like messaging and social networks, though it is incremental as it builds on existing techniques.
The paper tackled the problem of computationally expensive NLP models like BERT by adapting grouped convolutions from computer vision to self-attention layers, resulting in SqueezeBERT, which runs 4.3x faster than BERT-base on a Pixel 3 smartphone while maintaining competitive accuracy on the GLUE test set.
Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets, large computing systems, and better neural network models, natural language processing (NLP) technology has made significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. In particular, we consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today's highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. In this work, we observe that methods such as grouped convolutions have yielded significant speedups for computer vision networks, but many of these techniques have not been adopted by NLP neural network designers. We demonstrate how to replace several operations in self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test set. The SqueezeBERT code will be released.