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With deep neural networks (DNNs) emerging as the backbone in a multitude of computer vision tasks, their adoption in real-world applications broadens continuously.
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B. Fang, X. Zeng, F. Zhang, H. Xu, and M. Zhang, “FlexDNN: Input-Adaptive On-Device Deep Learning for Efficient Mobile Vision,” in
2020
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W. Liu, P. Zhou, Z. Wang, Z. Zhao, H. Deng, and Q. Ju, “FastBERT: a Self-distilling BERT with Adaptive Inference Time,” in
2020
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J. Xin, R. Tang, J. Lee, Y. Yu, and J. Lin, “DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference,” in
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2020
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S. Laskaridis, S. I. Venieris, M. Almeida, I. Leontiadis, and N. D. Lane, “SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud,” in
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F. Romero, Q. Li, N. J. Yadwadkar, and C. Kozyrakis, “INFaaS: Automated Model-less Inference Serving,” in
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S. Horvath, , S. Laskaridis, M. Almeida, I. Leontiadis, S. I. Venieris, and N. D. Lane, “FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout,” in
2021
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2021
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Z. Wang, W. Bao, D. Yuan, L. Ge, N. H. Tran, and A. Y. Zomaya, “Accelerating On-Device DNN Inference during Service Outage through Scheduling Early Exit,”
2020
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E. Qin, A. Samajdar, H. Kwon, V. Nadella, S. Srinivasan, D. Das, B. Kaul, and T. Krishna, “SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training,” in
2020
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-End Object Detection with Transformers,” in
2020
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N. P. Jouppi
2021
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S. Laskaridis, A. Kouris, and N. D. Lane, “Adaptive Inference through Early-Exit Networks: Design, Challenges & Directions,” in
2021
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M. Drumond, L. Coulon, A. Pourhabibi, A. C. Yüzügüler, B. Falsafi, and M. Jaggi, “Equinox: Training (for Free) on a Custom Inference Accelerator,” in
2021
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Y. Choi, Y. Kim, and M. Rhu, “Lazy Batching: An SLA-aware Batching System for Cloud Machine Learning Inference,” in
2021
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S. Laskaridis, S. I. Venieris, A. Kouris, R. Li, and N. D. Lane, “The Future of Consumer Edge-AI Computing,” in
2022
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A. Kouris, S. I. Venieris, S. Laskaridis, and N. D. Lane, “Multi-Exit Semantic Segmentation Networks,” in
2022
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Z. Liu, J. Song, C. Qiu, X. Wang, X. Chen, Q. He, and H. Sheng, “Hastening Stream Offloading of Inference via Multi-exit DNNs in Mobile Edge Computing,”
2022
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S. Mehta and M. Rastegari, “MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer,” in
2022
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2022
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Y. Liu, Y. Zhang, Y. Wang, F. Hou, J. Yuan, J. Tian, Y. Zhang, Z. Shi, J. Fan, and Z. He, “A Survey of Visual Transformers,”
2023
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S. Kim, C. Hooper, T. Wattanawong, M. Kang, R. Yan, H. Genc, G. Dinh, Q. Huang, K. Keutzer, M. W. Mahoney
2023
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