Research Status and Prospect of TinyML

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Integrated Circuits and Embedded Systems ›› 2023, Vol. 23 ›› Issue (2) : 7-11.

TOPICAL DISCUSS

Research Status and Prospect of TinyML

Wu Jianbang¹, Qiu Tian¹, Zhang Xin¹, Wu Peiwen¹, Lin Xiaoyan¹, Fu Xiao², Li Muyun², Ning Honglong²

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Abstract

In the paper,the definition,advantages and current problems of tiny machine learning are introduced.From exclusive or general tiny machine learning deployment methods,microprocessor design based on ARM Cortex-M or RISC-V,and deployment algorithms based on neural architecture search,the existing problems are discussed and the research status is introduced.Looking forward to the future development of tiny machine learning,it is believed that a full-featured tiny machine learning deployment framework is needed in the future,and hardware research is more based on RISC-V and hardware neural network acceleration units to form microprocessors,and how to improve search efficiency and reduce neural architecture search time.Finally,on the basis of the above,some thoughts are put forward on how to improve and develop the tiny machine learning ecology.

Key words

tiny machine learning / microcontroller / neural architecture search

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Wu Jianbang, Qiu Tian, Zhang Xin, Wu Peiwen, Lin Xiaoyan, Fu Xiao, Li Muyun, Ning Honglong. Research Status and Prospect of TinyML[J]. Integrated Circuits and Embedded Systems. 2023, 23(2): 7-11

References

[1] TinyML Foundation.About tinyML Foundation[EB/OL].[2022-11].https://www.tinyml.org/about/.
[2] Banbury C,Reddi V J,Lam M,et al.Benchmarking TinyML systems: Challenges and direction[J].arXiv preprint arXiv:2003.04821.
[3] David R,Duke J,Jain A,et al.Tensor Flow lite micro:Embedded machine learning for tinyml systems[C]//Proceedings of Machine Learning and Systems,2021:800-811.
[4] Pete W,Daniel S.TinyML:Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers [M].O'Reilly Media,Inc.2019:321-322.
[5] JanJongboom.Introducing EON:Neural Networks in Up to 55% Less RAM and 35% Less ROM[EB/OL].[2022-11]. https://www.edgeimpulse.com/blog/introducing-eon.
[6] Pham H T,Nguyen M A,Sun C C.AIoT solution survey and comparison in machine learning on low-cost microcontrolle[C]//In 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS).IEEE,2019:1-2.
[7] Lai L,Suda N,Chandra V.Cmsis-nn:Efficient neural network kernels for arm cortex-m cpus[J].arXiv preprint arXiv:1801.06601.
[8] Lai L,Suda N.Enabling deep learning at the LoT Edge[C]//In 2018 IEEE/ACM International Conference on Computer-Aided Design(ICCAD).IEEE,2018:1-6.
[9] STMicroelectronics.AI expansion pack for STM32Cu-beMX[EB/OL].[2022-11]. https://www.st.com/en/embedded-software/x-cube-ai.html#tools-software.
[10] 杨凯歌.Cortex-M3扩展可编程神经网络加速系统设计[D].西安:西安电子科技大学,2019.
[11] ARM.Introduction to the Armv8.1-M Architecture[EB/OL].[2022-11]. https://www.arm.com/resource-s/white-paper/intro-armv8-1-m-architecture.
[12] Schiavone PD,Rossi D,Pullini A,et al.Quentin: an ultra-low-power pulpissimo soc in 22nm fdx[C]//In 2018 IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference (S3S). IEEE,2018:1-3.
[13] Flamand E,Rossi D,Conti F,et al.GAP-8:A RISC-V SoC for AI at the Edge of the IoT[C]//2018 IEEE 29th International Conference on Application-specific Systems,Architectures and Processors (ASAP).IEEE,2018:1-4.
[14] Zhang S,Tong J,Zhang J,et al.A RISC-V Based Coprocessor Accelerator Technology Research for Convolution Neural Networks[C]//In Journal of Physics:Conference Series. IOP Publishing,2020:1631.
[15] WU N,JIANG T,ZHANGL,et al.A Reconfigurable Convolutional Neural Network-Accelerated Coprocessor Based on RISC-V Instruction Set[J].Electronics,2020,9(6):1005.
[16] 王松.基于RISC-V与CNN协处理器片上系统设计[D].西安:西安电子科技大学,2020.
[17] Gupta C,Suggala A S,Goyal A,et al.ProtoNN:Compressed and Accurate kNN for Resource-scarce Devices[C]//In International conference on machine learning. PMLR,2017:1331-1340.
[18] Kumar A,Goyal S,Varma M.Resource-efficient machine learning in 2 KB RAM for the internet of things[C]//In International conference on machine learning.PMLR,2017:1935-1944.
[19] Molchanov D,Ashukha A,Vetrov D.Variational dropout sparsifies deep neural networks[C]//Proceedings of the 34th International Conference on Machine Learning.PMLR 2017:70,2498-2507.
[20] Louizos C,Ullrich K,Welling M.Bayesian compression for deep learning[C]//Advances in neural information processing systems,2017.
[21] Fedorov I,Adams R P,Mattina M,et al.SpArSe: sparse architecture search for CNNs on resource-constrained microcontrollers[C]//In Proceedings of the 33rd International Conference on Neural Information Processing Systems,2019:4977-4989.
[22] Li Y,Chen Y,Dai X,et al.MicroNet: Towards image recognition with extremely low FLOPs[J].arXiv preprint arXiv:2011.12289.
[23] Lin J,Chen W M,Lin Y,et al.Mcunet:Tiny deep learning on iot devices[C]//Advances in Neural Information Processing Systems,2020:11711-11722.
[24] Lin J,Chen W M,Cai H,et al.Mcunetv2:Memory efficient patch-based inference for tiny deep learning[J].arXiv preprint arXiv:2110.15352.
[25] Fedorov I,Matas R,Tann H,et al.UDC:Unified DNAS for Compressible TinyML Models[J].arXiv preprint arXiv:2201.05842.
[26] Jacob B,Kligys S,Chen B,et al.Quantization and training of neural networks for efficient integer-arithmetic-only inference[C]//In Proceedings of the IEEE conference on computer vision and pattern recognition,2018:2704-2713.
[27] Wang K,Liu Z,Lin Y,et al.Haq:Hardware-aware automated quantization with mixed precision[C]//In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:8612-8620.
[28] Rusci M,Fariselli M,Capotondi A,et al.Leveraging automated mixed-low-precision quantization for tiny edge microcontrollers[C]//In IoT Streams for Data-Driven Predictive Maintenance and IoT,Edge,and Mobile for Embedded Machine Learning.Springer,2020:296-308.
[29] Torres-Sánchez E,Alastruey-Benedé J,Torres-Moreno E.Developing an AI IoT application with open software on a RISC-V SoC[C]//In 2020 XXXV Conference on Design of Circuits and Integrated Systems.IEEE,2020:1-6.