Neural Architecture Search for Transformers: A Survey
Open accessAbstract
Transformer-based Deep Neural Network architectures have gained tremendous interest due to their effectiveness in various applications across Natural Language Processing (NLP) and Computer Vision (CV) domains. These models are the de facto choice in several language tasks, such as Sentiment Analysis and Text Summarization, replacing Long Short Term Memory (LSTM) model. Vision Transformers (ViTs) have shown better model performance than traditional Convolutional Neural Networks (CNNs) in vision applications while requiring significantly fewer parameters and training time. The design pipeline of a neural architecture for a given task and dataset is extremely challenging as it requires expertise in several interdisciplinary areas such as signal processing, image processing, optimization and allied fields. Neural Architecture Search (NAS) is a promising technique to automate the architectural design process of a Neural Network in a data-driven way using Machine Learning (ML) methods. The search method explores several architectures without requiring significant human effort, and the searched models outperform the manually built networks. In this paper, we review Neural Architecture Search techniques, targeting the Transformer model and its family of architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Vision Transformers. We provide an in-depth literature review of approximately 50 state-of-the-art Neural Architecture Search methods and explore future directions in this fast-evolving class of problems.
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Searched transformer networks through NAS and HW-NAS often outperform manually designed models in accuracy and compute performance on the target hardware.
“The searched transformer networks through NAS and HW-NAS often outperform manually designed models in accuracy and compute performance on the target hardware [188].”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [188]
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A Survey of Techniques for Optimizing Transformer Inference
Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154 · see citing works
Self-citation
- Authors
- Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K.
- Affiliations
- Iowa State University (US)
- Argonne National Laboratory (US)
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 108374-108412
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4312257891
- Cited by
- 118
- References
- 33
- Citations by year
- 2026: 29 · 2025: 40 · 2024: 33 · 2023: 13 · 2022: 3
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Philip and Virginia Sproul Professorship
- National Science Foundation at Iowa State University (MRI 1726447, MRI 2018594)
- Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility (DEAC02-06CH11357)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- neural architecture search, transformer models, vision transformer, BERT, automated neural network design, search strategies, natural language processing, computer vision
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