Acknowledgement
이 논문은 2022년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임[No. 2022-0-00454, 스마트 엣지 디바이스 SW 개발 플랫폼 개발].
References
- P.P. Ray, "A review on TinyML: State-of-the-art and prospects," J. King Saud Univ.: Comput. Inf. Sci., vol. 34, no. 4, 2021.
- S. Soro, "TinyML for ubiquitous edge AI," arXiv preprint, CoRR, 2021, arXiv: 2102.01255.
- V. Rajapakse, I. Karunanayake, and N. Ahmed, "Intelligence at the extreme edge: A survey on reformable TinyML," arXiv preprint, CoRR, 2022, arXiv: 2204.00827.
- R. Sanchez Iborra and A.F. Skarmeta, "TinyML-Enabled frugal smart objects challenges and opportunities," IEEE Circuits Syst. Mag., vol. 20, no. 3, 2020, pp. 4-8. https://doi.org/10.1109/mcas.2020.3005467
- https://www.tinyml.org/
- 신성필, "서비스형 기계학습(MLaaS, Machine Learning as a Service) 시장 동향 및 기능 요구사항 표준," TTA저널, 제198호 제11/12월호, 2021.
- H. Doyu et al., "A tinymlaas ecosystem for machine learning in iot: Overview and research challenges," in Proc. Int. Symp. VLSI Des., Autom. Test (VLSI-DAT), (Hsinchu, Taiwan), Apr. 2021.
- C. Rogers, "The opportunity for AI at the edge and beyond," SensiML, Apr. 2020.
- 김대우, "2021년 머신러닝과 인공지능(AI) 트렌드-MLaaS," Jan. 2021, https://sqlermail.medium.com
- Vertiv, Data Center 2025-Closer To The Edge, 2019, https://www.vertiv.com/en-emea/about/news-and-insights/articles/pr-campaigns-reports/data-center-2025-closer-to-the-edge/
- https://aws.amazon.com/ko/sagemaker/edge/
- https://cloud.google.com/vertex-ai
- https://docs.microsoft.com/ko-kr/azure/iot-edge/
- https://octoml.ai/
- https://www.edgeimpulse.com/
- https://sensiml.com/
- Google Inc., TensorFlow Lite, https://www.tensorflow.org/lite/
- Google Inc., TensorFlow Lite for Microcontrollers, https://www.tensorflow.org/lite/microcontrollers
- Microsoft Corp., Embedded Learning Library, https://microsoft.github.io/ELL/
- ARM In., ARM-NN, https://www.arm.com/products/silicon-ip-cpu/ethos/arm-nn
- https://autokeras.com
- https://www.tiriasresearch.com/wp-content/uploads/2021/09/Edge-Impulse-Accelerates-MLOps-with-EON.pdf
- https://github.com/microsoft/nni
- S. Leroux et al., "TinyMLOps: Operational challenges for widespread edge AI Adoption," arXiv preprint, CoRR, 2022, arXiv: 2203.10923v2.
- M. Rastegari et al., "XnorNet: ImageNet classification using binary convolutional neural networks," arXiv preprint, CoRR, 2016, arXiv: 1603.05279.
- W. Chen, P. Wang, and J. Cheng, "Towards mixed-precision quantization o f neural networks via constrained cptimization," arXiv preprint, CoRR, 2021, arXiv: 2110.06554.
- K. Wang et al., "HanHAQ: Hardware-aware automated quantization," arXiv preprint, CoRR, 2019, arXiv: 1811.08886.
- Y. He et al., "Filter pruning via geometric median for deep convolutional neural networks acceleration," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., (Long Beach, CA, USA), June 2019, pp. 4340-4349.
- Y. He et al., "Amc: Automl for model compression and acceleration on mobile devices," in Proc. Eur. Conf. Comput. Vis. (ECCV), (Munich, Germany), Sept. 2018, pp. 784-800.
- J. Martinez et al., "Permute, quantize, and fine-tune: Efficient compression of neural networks," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., (Virtual), June 2021, pp. 15699-15708.
- T. Kim et al., "CPrune: Compiler-informed model pruning for efficient target-aware DNN execution," arXiv preprint, CoRR, 2022, arXiv: 2207.01260.
- T.J. Yang et al., "Netadapt: Platform-Aware neural network adaptation for mobile applications," in Proc. Eur. Conf. Comput. Vis. (ECCV), (Munich, Germany), Sept. 2018, pp. 285-300.
- K.T. Chitty-Venkata and A.K. Somani, "Neural architecture search survey: A hardware perspective," ACM Comput. Surv., 2022, doi: 10.1145/3524500.
- https://github.com/etri/nest-compiler
- X. Team, Xla: Domain-Specific Compiler for Linear Algebra that Optimizes Tensorflow Computations, 2019.
- W.F. Lin et al., "Onnc: A compilation framework connecting onnx to proprietary deep learning accelerators," in Proc. IEEE Int. Conf. Artif. Intell. Circuits Sys. (AICAS), (Hsinchu, Taiwan), Mar. 2019, pp. 214-218.
- S. Cyphers et al., "Intel nGraph: An intermediate representation, compiler, and executor for deep learning," arXiv preprint, CoRR, 2018, arXiv: 1801.08058.
- N. Rotem et al., "Glow: Graph lowering compiler techniques for neural networks," arXiv preprint, CoRR, 2018, arXiv: 1805.00907.
- T. Chen et al., "Tvm: An automated end-to-end optimizing compiler for deep learning," in Proc. USENIX Symp. Oper. Syst. Des. Implement., (Carlsbad, CA, USA), Oct. 2018, pp. 578-594.
- M. Li et al., "The deep learning compiler: A comprehensive survey," arXiv preprint, CoRR, 2020, arXiv: 2002.03794v4. https://doi.org/10.1109/TPDS.2020.3030548
- R. David et al., "TensorFlow lite micro: Embedded machine learning for TinyML systems," in Proc. Mach. Lear. Syst., (San Jose, CA, USA), 2021.
- Microsoft Corp., The Embedded Learning Library-Embedded Learning Library(ELL), 2022, Retrieved from https://microsoft.github.io/ELL/
- W. Li and M. Liewig, "A survey of AI accelerators for edge environment," in World Conference on Information Systems and Technologies, Springer, Cham, Switzerland, 2020, pp. 35-44.