Skip to content

Instantly share code, notes, and snippets.

@llhe

llhe/citations.md

Created March 15, 2024 14:30
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save llhe/3c29ced871e29f8f383806df92f215f3 to your computer and use it in GitHub Desktop.
Save llhe/3c29ced871e29f8f383806df92f215f3 to your computer and use it in GitHub Desktop.
Download ZIP
Citations of MACE and Mobile AI Bench projects
Raw
citations.md
Title of the paper Project cited Published media Download URL FIELD5 FIELD6 FIELD7 FIELD8 FIELD9 FIELD10 FIELD11 FIELD12 FIELD13 FIELD14 FIELD15 FIELD16
MLPerf Mobile Inference Benchmark mobile-ai-bench MLSys 2022 https://proceedings.mlsys.org/paper_files/paper/2022/file/a2b2702ea7e682c5ea2c20e8f71efb0c-Paper.pdf
AI Tax in Mobile SoCs: End-to-end Performance Analysis of Machine Learning in Smartphones mobile-ai-bench 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9408206&casa_token=nzbVEkCmkpwAAAAA:h4xeS2ABmMe5Sb7AC0o98gKs5yqmFJVrwEGhWkrBp2hvj21_ifNdw1q8WV4zrMCLVsKRlGj4F0BU3A
AIPerf: large-scale AI system benchmark mobile-ai-bench Big Data Research in Chinese https://www.infocomm-journal.com/bdr/article/2021/2096-0271/2096-0271-7-3-00153.shtml
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey mace IEEE COMMUNICATIONS SURVEYS & TUTORIALS https://arxiv.org/pdf/1907.08349.pdf
A Comprehensive Benchmark of Deep Learning Libraries on Mobile Devices mace WWW 22 https://arxiv.org/pdf/2202.06512.pdf
A Comprehensive Deep Learning Library Benchmark and Optimal Library Selection IEEE Transactions on Mobile Computing https://ieeexplore.ieee.org/abstract/document/10209228?casa_token=DglaGUiGsNwAAAAA:UVxNjI6iMOVyrbP5N-quUVlGIm8aBDbPxyhVZqkNPMlkXc8vvU-eYQJuN84-bmbD2cvzvbkhd3__mq8
A first look at deep learning apps on smartphones mace WWW'19 https://arxiv.org/pdf/1812.05448.pdf
A survey on deploying mobile deep learning applications: A systemic and technical perspective mace Digital Communications and Networks https://www.sciencedirect.com/science/article/pii/S2352864821000298
AI-driven Mobile Apps: an Explorative Study mace arXiv preprint arXiv:2212.01635, 2022 https://arxiv.org/pdf/2212.01635.pdf
AWARE-CNN: Automated Workflow for Application-Aware Real-Time Edge Acceleration of CNNs mace IEEE Internet of Things Journal https://ieeexplore.ieee.org/abstract/document/9078049
Band: coordinated multi-DNN inference on heterogeneous mobile processors mace 20th MobiSys 2022 https://dl.acm.org/doi/abs/10.1145/3498361.3538948
CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices mace MobiSys '22: Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services https://chrisplus.me/assets/pdf/mobisys22-CoDL.pdf
Challenges and Obstacles Towards Deploying Deep Learning Models on Mobile Devices mace arXiv:2105.02613 https://arxiv.org/pdf/2105.02613.pdf
Deep learning on mobile devices: a review mace Proc. SPIE 10993, Mobile Multimedia/Image Processing, Security, and Applications 2019 https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10993/109930A/Deep-learning-on-mobile-devices-a-review/10.1117/12.2518469.short#_=_
Deep Learning Development Status in China mace The Development of Deep Learning Technologies, Book by Springer https://link.springer.com/chapter/10.1007/978-981-15-4584-9_2
Design and Research of the AI Badminton Model Based on the Deep Learning Neural Network mace Journal of Mathematics https://www.hindawi.com/journals/jmath/2022/6739952/
Enabling Real-time AI Inference on Mobile Devices via GPU-CPU Collaborative Execution mace IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) https://ieeexplore.ieee.org/abstract/document/9904808
Edge Intelligence: Challenges and Opportunities mace International Conference on Computer, Information and Telecommunication Systems (CITS) https://ieeexplore.ieee.org/abstract/document/9232575
Heimdall: mobile GPU coordination platform for augmented reality applications mace MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking https://dl.acm.org/doi/abs/10.1145/3372224.3419192
IBD1: The metrics and evaluation method for DNN processor benchmark while doing Inference task mace Journal of Intelligent & Fuzzy Systems 40(1):1-13 https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs202552
Low-level Optimizations for Faster Mobile Deep Learning Inference Frameworks mace MM '20: Proceedings of the 28th ACM International Conference on Multimedia https://dl.acm.org/doi/abs/10.1145/3394171.3416516
Mobile-Cloud Cooperative Deep Learning Platform for Mixed Reality Applications mace SMS ’21, June 24, 2021, Virtual, WI, USA https://juheonyi.github.io/files/sms21.pdf
MoGA: Searching Beyond Mobilenetv3 mace International Conference on Acoustics, Speech, and Signal Processing (ICASSP) https://ieeexplore.ieee.org/abstract/document/9054428
On-Device Neural Net Inference with Mobile GPUs mace Efficient Deep Learning for Computer Vision CVPR 2019 (ECV2019) https://arxiv.org/abs/1907.01989
nn-Meter: towards accurate latency prediction of deep-learning model inference on diverse edge devices mace MobiSys '21: Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services https://dl.acm.org/doi/abs/10.1145/3458864.3467882
Robustness of on-Device Models: Adversarial Attack to Deep Learning Models on Android Apps mace IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP) https://ieeexplore.ieee.org/abstract/document/9402124
Seesaw-Net: Convolution Neural Network With Uneven Group Convolution mace arXiv:1905.03672 https://arxiv.org/abs/1905.03672
Understanding Real-world Threats to Deep Learning Models in Android Apps mace CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security https://dl.acm.org/doi/abs/10.1145/3548606.3559388
MNN: A Universal and Efficient Inference Engine mace MLSys 20 https://arxiv.org/abs/2002.12418
Romou: rapidly generate high-performance tensor kernels for mobile GPUs mace MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking https://dl.acm.org/doi/abs/10.1145/3495243.3517020
A Survey of Open-source Tools for FPGA-based Inference of Artificial Neural Networks mace 2021 Ivannikov Memorial Workshop (IVMEM) https://ieeexplore.ieee.org/abstract/document/9693757?casa_token=pJEH_JNCdVUAAAAA:KKtjB9qs-T9DWQ5vIdf2bR7xktjq0pvlms0YA2a1Irm_KT6tV2fVuxqyX2m7Asx7HiYY44LaTlOpS9w
Deep Learning Models for Audio Processing Applications Under Resource-Constrained Devices: A Survey mace Book series - Communications in Computer and Information Science(CCIS,volume 1800) https://link.springer.com/chapter/10.1007/978-3-031-31327-1_12
SwapNet: Efficient Swapping for DNN Inference on Edge AI Devices Beyond the Memory Budget mace IEEE Transactions on Mobile Computing https://ieeexplore.ieee.org/abstract/document/10403957?casa_token=BcOHxg4ose8AAAAA:URXiuY7r2R_b2Clbip3EgWU0XStFr-aZTFVUNG4kAFqSTJxQWJ41ya13vDCx0VEZ6f77yUzr7wvRMdk
DNNTune: Automatic Benchmarking DNN Models for Mobile-cloud Computing mace ACM Transactions on Architecture and Code Optimization https://dl.acm.org/doi/abs/10.1145/3368305
Enabling Real-time AI Inference on Mobile Devices via GPU-CPU Collaborative Execution mace IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) https://ieeexplore.ieee.org/abstract/document/9904808?casa_token=XVOX8dbWUJYAAAAA:uyghTsS9-98Jf5eFHqYOxyCELPt_m5_dBxUFwDEGp5y4Q6I9H3W_-PPICDtnPllqk1EbV-jQs_Wr3VM
Edge Intelligence: Challenges and Opportunities mace International Conference on Computer, Information and Telecommunication Systems (CITS) https://ieeexplore.ieee.org/abstract/document/9232575?casa_token=O9-60PePlK0AAAAA:L1N3j6Fu1z6cjEHQuBSEtE-vZT69eOKdkkd-7oWyxW0f2z6-G8Bpmwd1kmprp5_52p0si1VQKzuiuPw
Design Space Exploration of Accelerators and End-to-End DNN Evaluation with TFLITE-SOC mace International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) https://ieeexplore.ieee.org/abstract/document/9235056?casa_token=zbJLAIiYWr0AAAAA:-w5P_x8B9bOYgz2wvvHN2KQBKfCATLjNpA3wc9BwXkDtg9VIZQCghQqtDb2-cZSdovTWjyf2ZhfDY_0
Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Apps mace USENIX Secutity https://www.usenix.org/conference/usenixsecurity21/presentation/sun-zhichuang
Automated Backend Allocation for Multi-Model, On-Device AI Inference mace Proceedings of the ACM on Measurement and Analysis of Computing Systems https://dl.acm.org/doi/abs/10.1145/3626793
Mobile Deployment Method for Defect Recognition Algorithms in Power Transmission Scenarios mace 2023 International Conference on Data Science and Network Security (ICDSNS) https://ieeexplore.ieee.org/abstract/document/10244925?casa_token=M0TYkhlb6bkAAAAA:kXDftLq9hJ0dp4vb90FKrZGqJdZU6GiO5_2M26rIADBmXfY_SyfYkY95mHt0aZyh-2Ql1EFTWJtYhQ
NN-Stretch: Automatic Neural Network Branching for Parallel Inference on Heterogeneous Multi-Processors mace MobiSys '23: Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services https://dl.acm.org/doi/abs/10.1145/3581791.3596870
A First Look at On-device Models in iOS Apps mace ACM Trans. Softw. Eng. Methodol., https://arxiv.org/pdf/2307.12328.pdf
Design and Implementation of Convolutional Neural Network Accelerator Based on RISCV mace Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 170) https://link.springer.com/chapter/10.1007/978-3-031-29097-8_53
Design and implementation of neural network computing framework on Zynq SoC embedded platform mace 10th International Conference of Information and Communication Technology (ICICT-2020) https://pdf.sciencedirectassets.com/280203/1-s2.0-S1877050921X00063/1-s2.0-S1877050921005676/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEJH%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIE%2BmrPexAou20UlhC3Uk3zgHYd2qrGrnCJzsopFUHq6iAiEA6jZhXo1y2TgdKJuJACR0lnQS3pMET0x0aVsBYpxzSXcqsgUIWhAFGgwwNTkwMDM1NDY4NjUiDNMrc8uoBo%2Bcoj4mxCqPBYwGW0s4lWI8SW8zbHN43IYacQbIIhMKJbzAdAT2zWRCqQrtkScj9tY4%2FVT3OabgpmBgKIaDTcl6ATEtROYRAygPx8l42OtgBu0ih4Nfd0wzoqS83ONx5KZfInFQ%2Feoo1NNXzRZnS8vWTxQg9TZXYqCVSLk0267JlrE%2Fa1jQDjbXBGYVmdvZQOQ%2Fk0MlsO9cIBeBATRZzC%2BhUDkgMp4vVLdDzglI9uMEuepCfkawDGPZUzWSYGTzUr6rlOQ8d5Wwhf%2BdhWuKmCUYCryIU%2BIDNRaGquK%2FKGd90e3JKov1TCqqWr3mgW8ZUbsxWPElCRcuv%2F9SbMKeF6p%2FLIss%2Biy8b6KmpFvSiaCNUZPzhFz4a2SECceIgDa5SN0G7MEEYkbfjD1CzIDRg38QIzAZMhm1DDZo7mtxwT07rsfCvUEvqTC0RJTHFRPrLDFlD89rwRPpiNFZwGoVLzoubS%2BmaSW%2B%2BqulJtqLRJoGKeoVqVWgBREKoXrSYCGX5wA7ngOW3Kd%2BPVyUtM152S6wWtjbShZFUcrPlIzJKMGwr0FvavMEX5SG%2FiciV4pofXihZD6%2F2Lu%2BW594O994gQg0PrPEEQ7aGBxsMCphuEvZRD2AQD%2Ft5EUpCCTVVABHvsuWMKhg9y0WwZnSkXiTYB2xRCjdZb6AFSii14ur9fNULExRcTT%2FxXS7oeM8F5mJ7%2Bng8wMOZYghoeRj0A0MpR05%2FrkWV6X9%2FLmwExutivupZidTVMWvtPtvJg%2BMihSN%2FTCaIj9NHieTmCW6R7%2F38duElLO0gpzm0m6Op49gxx2CUQgtioHgE42LR44h%2FreMWjJieQinAv5FQ3SvSXPhKr4P50qdwcYBixXGXWtK6ryz9ble1Ec%2FV4Uw5NPyrQY6sQHvx0aXYhx%2FxHPoqC%2BhHHHXGp4GotHf3gFOkbXurzu9HQMHoIfVR2TIj%2FEe4XtKWcR5UYhum5sZbEyc3%2BSbRf7s9ZcbbXbf8swdW1wsXlhLWHwqBDh3MC%2FqXO15yQyhfc16Ig6tdavfsiOHa46TMmTZVHu9Cmkfz2GHqCcE69kN4wavBxGsbZjYx%2B6b7sqhjm1f%2BKiRisUSD7XFJztBviq%2Fk%2FQW21VKSfEX5bYo3UxjHsk%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240202T095334Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYTRGOIVVO%2F20240202%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=735cbf8763f49a0a76dad9a06d1c590bd0878929b55bf5f1721ece60ccd8515c&hash=66580c2cf2bfd934d5580bbe5b5cf91d29fa61b5327d57d2b95688ac4055aa6d&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1877050921005676&tid=spdf-646be31b-ff86-4d53-b795-297f22bb5845&sid=9814aaa41b4b14424b9a6f4-63e0d4294930gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=080e5e5509535a0c&rr=84f18c3defb85e07&cc=hk
ParallelFusion: Towards Maximum Utilization of Mobile GPU for DNN Inference mace EMDL ’21, June 25, 2021, Virtual, WI, USA https://air.tsinghua.edu.cn/pdf/ParallelFusion-Towards-Maximum-Utilization-of-Mobile-GPU-for-DNN-Inference.pdf
A Deep Learning Method for the Health State Prediction of Lithium-Ion Batteries Based on LUT-Memory and Quantization mace World Electric Vehicle Journal 2024 https://www.mdpi.com/2032-6653/15/2/38
Block Shuffle: A Method for High-Resolution Fast Style Transfer With Limited Memory mace IEEE Access ( Volume: 8) https://ieeexplore.ieee.org/document/9179737
Design Space Exploration of Accelerators and End-to-End DNN Evaluation with TFLITE-SOC mace International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) https://ieeexplore.ieee.org/document/9235056
Efficient Neural Network Inference for Resource mace PhD thesis https://ddd.uab.cat/pub/tesis/2023/hdl_10803_688291/jbc1de1.pdf
Efficient Neural Network Inference for Resource Constrained Devices mace PhD thesis https://espace.library.uq.edu.au/data/UQ_7f1b499/s4523312_phd_thesis.pdf?dsi_version=13cb120cfae648a313dcd37efa249229&Expires=1706954779&Key-Pair-Id=APKAJKNBJ4MJBJNC6NLQ&Signature=R1nOeJYJgIUKIJngVvQhDKmP8VAMcn6ogS8z5jZQ0LfEsJD95xYeUH9xaYkEdhLHERPpYpxLNiKOQUc25x6bHHqYMAhFduK1N1K5ykWld4LqcWcxs1dT3LMhmtoJW1SsR2BsUOq6gqJCDHztgjPzKZqz7xdb37W-kU7AIPW8mT~2AgsFMgs1cEIlpBlVSLn9PfZMw79sL1wc7nn99r9AUycIGNWXpSx6wrpQGWl9NWokzg-Oyfz2~IB0pK7NXiwhBXOe~~uEFZJx8yyYlsOCst2DbH3w3KKs6LOxQuyVDVhNEKsZHXO-Db8wlTT7r6Ij5vEy~TG2Y5NRWhUOIP0u7w__
Towards Low-cost and Real-time Mobile Sensing mace PhD thesis https://scholars.hkbu.edu.hk/ws/portalfiles/portal/56785361/RPG_PHD_2021_10_20_E2_R36_T.pdf
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment