IEEE Circuits and Systems Magazine - Q3 2020 - 18

[15] H.-T. Pham, M.-A. Nguyen, and C.-C. Sun, "AIoT solution survey and
comparison in machine learning on low-cost microcontroller," in Proc.
IEEE Int. Symp. Intelligent Signal Processing and Communication Systems
(ISPACS), Dec. 2019, pp. 1-2. [Online]. Available: https://ieeexplore.ieee
.org/document/8986357/
[16] V. Falbo, T. Apicella, D. Aurioso, L. Danese, F. Bellotti, R. Berta, and
A. De Gloria, "Analyzing machine learning on mainstream microcontrollers," in Proc. Int. Conf. Applications Electronics Pervading Industry,
Environment and Society, 2020, pp. 103-108. [Online]. Available: https://
link.springer.com/chapter/10.1007/978-3-030-37277-4_12
[17] R. Costa, "The internet of moving things [industry view]," IEEE Technol.
Soc. Mag., vol. 37, no. 1, pp. 13-14, Mar. 2018. doi: 10.1109/MTS.2018.2795092.
[Online]. Available: http://ieeexplore.ieee.org/document/8307131/
[18] M. Quigley and M. Burke, "Low-cost Internet of Things digital technology adoption in SMEs," Int. J. Manage. Pract., vol. 6, no. 2, p. 153,
2013. doi: 10.1504/IJMP.2013.055828. [Online]. Available: http://www
.inderscience.com/link.php?id=55828
[19] R. Sanchez-Iborra and M.-D. Cano, "State of the art in LP-WAN solutions for industrial IoT services," Sensors, vol. 16, no. 5, p. 708, 2016. doi:
10.3390/s16050708.
[20] W. Niu et al., "PatDNN: Achieving real-time DNN execution on mobile devices with pattern-based weight pruning," in Proc. 25th Int. Conf.
Architectural Support Programming Languages and Operating Systems,
New York, Mar. 2020, pp. 907-922. doi: 10.1145/3373376.3378534.
[Online]. Available: https://dl.acm.org/doi/10.1145/3373376.3378534
[21] C. R. Banbury et al., "Benchmarking TinyML systems: Challenges
and direction," in Proc. 1st Int. Workshop Benchmarking Machine Learning
Workloads Emerging Hardware - 3rd Conf. Machine Learning and Systems
(MLSys), 2020. [Online]. Available: https://arxiv.org/pdf/2003.04821.pdf
[22] E. Liberis and N. D. Lane, Neural networks on microcontrollers:
saving memory at inference via operator reordering. 2019.
[23] P. Randhawa, V. Shanthagiri, and A. Kumar, "A review on applied
machine learning in wearable technology and its applications," in Proc.
IEEE Int. Conf. Intelligent Sustainable Systems (ICISS), Dec. 2017, pp. 347-
354. [Online]. Available: https://ieeexplore.ieee.org/document/8389428/
[24] E. Ahmed, I. Yaqoob, A. Gani, M. Imran, and M. Guizani, "Internetof-things-based smart environments: state of the art, taxonomy, and
open research challenges," IEEE Wireless Commun., vol. 23, no. 5, pp.
10-16, Oct. 2016. doi: 10.1109/MWC.2016.7721736. [Online]. Available:
http://ieeexplore.ieee.org/document/7721736/
[25] A. Rizzardi, D. Miorandi, S. Sicari, C. Cappiello, and A. Coen-Porisini, "Networked smart objects: Moving data processing closer to the
source," in (Lecture Notes Institute Computer Sciences, Social Informatics
and Telecommunications Engineering), 2016, pp. 28-35. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-319-47075-7_4
[26] A. Roy, A. M. Zalzala, and A. Kumar, "Disruption of things: A model
to facilitate adoption of IoT-based innovations by the urban poor,"
Proc. Eng., vol. 159, pp. 199-209, 2016. doi: 10.1016/j.proeng.2016.08.159.
[Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/
S1877705816323074
[27] J. Santa and P. J. Fernández, "Seamless IPv6 connectivity for twowheelers," Pervas. Mobile Comput., vol. 42, pp. 526-541, Dec. 2017. doi:
10.1016/j.pmcj.2017.09.002. [Online]. Available: https://www.sciencedirect
.com/science/article/pii/S1574119217300652
[28] M. Wollschlaeger, T. Sauter, and J. Jasperneite, "The future of industrial communication: Automation networks in the era of the Internet of Things and industry 4.0," IEEE Ind. Electron. Mag., vol. 11, no. 1,
pp. 17-27, Mar. 2017. doi: 10.1109/MIE.2017.2649104. [Online]. Available:
http://ieeexplore.ieee.org/document/7883994/
[29] S. Deshmukh and R. Shah, "Computation offloading frameworks
in mobile cloud computing: A survey," in Proc. IEEE Int. Conf. Current
Trends Advanced Computing (ICCTAC), Mar. 2016, pp. 1-5. [Online].
Available: http://ieeexplore.ieee.org/document/7567332/
[30] P. Jayaraman, A. Yavari, D. Georgakopoulos, A. Morshed, and A.
Zaslavsky, "Internet of Things platform for smart farming: Experiences
and lessons learnt," Sensors, vol. 16, no. 11, p. 1884, Nov. 2016. doi: 10.3390/
s16111884. [Online]. Available: http://www.mdpi.com/1424-8220/16/11/1884
[31] P. Warden and D. Situnayake, Tinyml: Machine Learning with tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers. O'Reilly
UK, 2019.
[32] TensorFlow. 2020. [Online]. Available: https://www.tensorflow.org/
[33] Scikit-learn. 2020. [Online]. Available: https://scikit-learn.org/
[34] PyTorch. 2020. [Online]. Available: https://pytorch.org/

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[35] TensorFlow Lite. 2020. [Online]. Available: https://www.tensorflow
.org/lite/
[36] TinyGo. 2020. [Online]. Available: https://tinygo.org/
[37] Embedded Learning Library. 2020. [Online]. Available: https://
microsoft.github.io/ELL/
[38] The Microsoft Cognitive Toolkit. 2020. [Online]. Available: https://
docs.microsoft.com/en-us/cognitive-toolkit/
[39] Darknet: Convolutional Neural Networks. 2020. [Online]. Available:
https://github.com/pjreddie/darknet
[40] Open Neural Network Exchange. 2020. [Online]. Available: https://
onnx.ai/
[41] ARM-NN. 2020. [Online]. Available: https://github.com/ARM
-software/armnn
[42] CMSIS-NN. 2020. [Online]. Available: https://arm-software.github
.io/CMSIS_5/NN/html/
[43] STM32Cube.AI 2020, [Online]. Available: https://www.st.com/en/
embedded-software/x-cube-ai.html
[44] AIfES. 2020. [Online]. Available: https://www.ims.fraunhofer.de/
en/Business_Units_and_Core_Competencies/Electronic-Assistance
-Systems/Technologies/Artificial-Intelligence-for-Embedded-Systems
-AIfES.html
[45] NanoEdge AI Studio. 2020. [Online]. Available: https://cartesiam.ai/
[46] MicroML. 2020. [Online]. Available: https://github.com/eloquentarduino/
micromlgen
[47] D. Morawiec, sklearn-porter, 2020. [Online]. Available: https://
github.com/nok/sklearn-porter
[48] m2cgen. 2020. [Online]. Available: https://github.com/BayesWitnesses/
m2cgen
[49] Weka-porter. 2020. [Online]. Available: https://github.com/nok/
weka-porter
[50] L. Tsutsui da Silva, V. M. A. Souza, and G. E. A. P. A. Batista, "EmbML
Tool: Supporting the use of supervised learning algorithms in low-cost
embedded systems," in Proc. IEEE 31st Int. Conf. Tools Artificial Intelligence (ICTAI), Nov. 2019, pp. 1633-1637. [Online]. Available: https://
ieeexplore.ieee.org/document/8995408/
[51] J. Nordby, "emlearn: Machine Learning inference engine for microcontrollers and embedded devices," Mar. 2019. [Online]. Available:
https://doi.org/10.5281/zenodo.2589394
[52] uTensor. 2020. [Online]. Available: https://github.com/uTensor/uTensor
[53] TinyMLgen. 2020. [Online]. Available: https://github.com/eloquen
tarduino/tinymlgen
[54] A. Capotondi, M. Rusci, M. Fariselli, and L. Benini, "CMix-NN: Mixed
low-precision CNN library for memory-constrained edge devices," IEEE
Trans. Circuits Syst. II, Express Briefs, to be published. [Online]. Available: https://ieeexplore.ieee.org/document/9049084/
[55] X. Wang, M. Magno, L. Cavigelli, and L. Benini, "FANN-on-MCU: An
open-source toolkit for energy-efficient neural network inference at the
edge of the Internet of Things," IEEE Internet Things J., to be published.
doi: 10.1109/JIOT.2020.2976702. [Online]. Available: https://ieeexplore
.ieee.org/document/9016202/
[56] Linaro. 2020. [Online]. Available: https://www.linaro.org/
[57] Caffe. 2020. [Online]. Available: https://caffe.berkeleyvision.org/
[58] Keras. 2020. [Online]. Available: https://keras.io/
[59] G. Holmes, A. Donkin, and I. Witten, "WEKA: a machine learning
workbench," in Proc. IEEE ANZIIS '94 - Australian New Zealnd Intelligent
Information Systems Conf., 1994, pp. 357-361. [Online]. Available: http://
ieeexplore.ieee.org/document/396988/
[60] Teensy. 2020. [Online]. Available: https://www.pjrc.com/teensy/
[61] Mbed platforms. 2020. [Online]. Available: https://os.mbed.com/
platforms/
[62] A. G. Howard et al., "MobileNets: Efficient convolutional neural
networks for mobile vision applications," Apr. 2017. [Online]. Available:
http://arxiv.org/abs/1704.04861
[63] Fast Artificial Neural Network Library (FANN). 2020. [Online].
Available: http://leenissen.dk/fann/wp/
[64] D. Rossi et al., "PULP: A parallel ultra low power platform for next
generation IoT applications," in Proc. IEEE Hot Chips 27 Symp. (HCS),
Aug. 2015, pp. 1-39. [Online]. Available: http://ieeexplore.ieee.org/
document/7477325/
[65] Y Wang, Z Luo, G Qiu, and G Zhou, "Design of Smart Watch system
based on E-paper," in Proc. 10th IEEE Int. Conf. Nano/Micro Engineered
and Molecular Systems, Apr. 2015, pp. 327-330. [Online]. Available: http://
ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7147437

IEEE CIRCUITS AND SYSTEMS MAGAZINE 		

THIRD QUARTER 2020
IEEE Circuits and Systems Magazine - Q3 2020

Table of Contents for the Digital Edition of IEEE Circuits and Systems Magazine - Q3 2020
