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SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
Open accessAbstract
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.Toward this end, 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.To begin to address this problem, we draw inspiration from the computer vision community, where work such as MobileNet has demonstrated that grouped convolutions (e.g., depthwise convolutions) can enable speedups without sacrificing accuracy.We demonstrate how to replace several operations in self-attention layers with grouped convolutions and use this technique in a novel network architecture called Squeeze-BERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test set.A PyTorch-based implementation of Squeeze-BERT is available as part of the Hugging Face Transformers library: https:// huggingface.co/squeezebert
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“Moreover, models such as MobileBERT and SqueezeBERT employ bottleneck layers and grouped convolutions, leading to a 3× to 4× reduction in latency while maintaining >90% of the original model’s accuracy.”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [103]
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Metadata
- Authors
- Iandola, F., Shaw, A., Krishna, R., & Keutzer, K.
- Affiliations
- University of California, Berkeley (US)
- Venue
- Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing
- Published
- 2020
- Pages
- 124-135
- OpenAlex
- W3036463250
- Cited by
- 114
- References
- 77
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 10 · 2023: 30 · 2022: 21 · 2021: 27 · 2020: 5
- FWCI
- 6.40
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Simple and Efficient Natural Language Processing (SustaiNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Samsung
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- efficient neural networks, group convolution, depthwise convolution, self-attention, BERT, RoBERTa, mobile deployment, computational efficiency, GLUE benchmark
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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