ViT-Reg: Regression-Focused Hardware-Aware Fine-Tuning for ViT on TinyML Platforms

Editor’s notes: This article presents a hardware-aware methodology for modeling the performance and energy behavior of vision transformers (ViTs) across a wide design space relevant to TinyML platforms. It serves as a valuable framework for balancing accuracy and efficiency in deploying ViTs on resource-constrained edge systems such as autonomous robotics. —Theocharis Theocharides, University of Cyprus, Cyprus —Marian Verhelst, KU Leuven, Belgium —Vijay Janapa Reddy, Harvard University, USA —Evgeni Gousev, Qualcomm, USA

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Publication Details

Journal
IEEE Design and Test
Published
2024-12-23
DOI
https://doi.org/10.1109/mdat.2024.3521320
Citations
2
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.98
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article

ViT-Reg: Regression-Focused Hardware-Aware Fine-Tuning for ViT on TinyML Platforms

Arnab Neelim Mazumder, Tinoosh Mohsenin, Md. Ragib Shaharear
2 citations
IEEE Design and Test
Parallel Computing and Optimization Techniques
0.98
article

ViT-Reg: Regression-Focused Hardware-Aware Fine-Tuning for ViT on TinyML Platforms

Arnab Neelim Mazumder, Tinoosh Mohsenin, Md. Ragib Shaharear
article en
2 citations

Abstract

Editor’s notes: This article presents a hardware-aware methodology for modeling the performance and energy behavior of vision transformers (ViTs) across a wide design space relevant to TinyML platforms. It serves as a valuable framework for balancing accuracy and efficiency in deploying ViTs on resource-constrained edge systems such as autonomous robotics. —Theocharis Theocharides, University of Cyprus, Cyprus —Marian Verhelst, KU Leuven, Belgium —Vijay Janapa Reddy, Harvard University, USA —Evgeni Gousev, Qualcomm, USA

IEEE Design and TestVol. 42(5)
Johns Hopkins University (US), University of Maryland, Baltimore County (US)
Openalex Percentile: Top 22%
Parallel Computing and Optimization Techniques
0.98
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