@conference{nokey,

title = {Demo Abstract: Online Training and Inference for On-Device Monocular Depth Estimation},

author = {Allen-Jasmin Farcas and Geffen Cooper and Hyun Joon Song and Afnan Mir and Vincent Liew and Chloe Tang and Prithvi Senthilkumar and Tiani Chen-Troester and Radu Marculescu},

url = {https://ieeexplore.ieee.org/abstract/document/10562188},

year  = {2024},

date = {2024-05-13},

booktitle = {Proceedings of the 9th ACM/IEEE Conference on Internet of Things Design and Implementation},

keywords = {Edge AI, Efficient Inference, Monocular Depth Estimation, On-device Training},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {Efficient Low-rank Backpropagation for Vision Transformer Adaptation},

author = {Yuedong Yang and Hung-Yueh Chiang and Guihong Li and Diana Marculescu and Radu Marculescu},

url = {https://radum.ece.utexas.edu/wp-content/uploads/2023/09/LBP_WHT.pdf},

year  = {2023},

date = {2023-12-11},

urldate = {2023-12-11},

publisher = {Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS) 2023},

abstract = {The increasing scale of vision transformers (ViT) has made the efficient fine-tuning of these large models for specific needs a significant challenge in various applications. This issue originates from the computationally demanding matrix multiplications required during the backpropagation process through linear layers in ViT. In this paper, we tackle this problem by proposing a new Low-rank BackPropagation via Walsh-Hadamard Transformation (LBP-WHT) method. Intuitively, LBP-WHT projects the gradient into a low-rank space and carries out backpropagation. This approach substantially reduces the computation needed for adapting ViT, as matrix multiplication in the low-rank space is far less resource-intensive. We conduct extensive experiments with different models (ViT, hybrid convolution-ViT model) on multiple datasets to demonstrate the effectiveness of our method. For instance, when adapting an EfficientFormer-L1 model on CIFAR100, our LBP-WHT achieves 10.4% higher accuracy than the state-of-the-art baseline, while requiring 9 MFLOPs less computation. As the first work to accelerate ViT adaptation with low-rank backpropagation, our LBP-WHT method is complementary to many prior efforts and can be combined with them for better performance.},

keywords = {On-device Training, Vision Transformer},

pubstate = {published},

tppubtype = {conference}

}

@conference{albertcvpr2023,

title = {Efficient On-device Training via Gradient Filtering},

author = {Yuedong Yang and Guihong Li and Radu Marculescu},

url = {https://arxiv.org/pdf/2301.00330.pdf},

year  = {2023},

date = {2023-02-27},

urldate = {2023-02-27},

booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

keywords = {Edge AI, Internet of Things, On-device Training, Systems},

pubstate = {published},

tppubtype = {conference}

}