Abstract
In the last few years, microcontrollers used for IoT devices became more and more powerful, and many authors have started to use them in machine learning systems. Most of the authors used them just for data collecting for ML algorithms in the clouds, but some of them implemented ML algorithms on the microcontrollers. The goal of this paper is to analyses the neural networks data propagation speed of one popular SoC (Espressif System company ESP32) with simple neural networks implementation with two different development environment, Arduino IDE and MycroPython. Neural networks with one hidden layer are used with a different number of neurons. This SoC is analysed because some companies started to produce them with UXGA (Ultra Extended Graphics Array) camera implemented and it can be used to distribute computing load from central ML servers.
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References
Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the Internet of Things with edge computing. IEEE Network 32(1), 96–101 (2018)
Espressif System: ESP32 Overview. https://www.espressif.com/en/products/hardware/esp32/overview. Accessed 15 June 2019
Biswas, S.B., Iqbal, M.T.: Solar water pumping system control using a low cost ESP32 microcontroller. In: IEEE Canadian Conference on Electrical & Computer Engineering, Quebec City (2018)
Abdullah, A.H., et al.: Development of ESP32-based Wi-Fi electronic nose system for monitoring LPG leakage at gas cylinder refurbish plant. In: 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), Kuching (2018)
Maier, A., Sharp, A., Vagapov, Y.: Comparative analysis and practical implementation of the ESP32 microcontroller module for the Internet of Things. In: 7th IEEE International Conference on Internet Technologies and Applications, Wrexham (2017)
Zidek, K., Janacova, D., Hosovsky, A., Pitel, J., Lazorik, P.: Data optimization for communication between wireless IoT devices and cloud platforms in production process. In: 3rd EAI International Conference on Management of Manufacturing Systems, Dubrovnik (2018)
Rosato, D., Masciadri, A.: Non-invasive monitoring system to detect siting people. In: Goodtechs, Bologna (2018)
Islam Chowdhuryy, M.H., Sultana, M., Ghosh, R., Ahamed, J.U., Mahmood, M.: AI assisted portable ECG for fast and patient specific diagnosis. In: International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, Rajshahi (2018)
Fernoaga, V.P., Stelea, G.-A., Balan, A., Sandu, F.: OCR-based solution for the integration of legacy and-or non-electric counters in cloud smart grids. In: IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME), Iași (2018)
Komarek, A., Pavlik, J., Mercl, L., Sobeslav, V.: Hardware layer of ambient intelligence environment implementation. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10449, pp. 325–334. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67077-5_31
Chand, G., Ali, M., Barmada, B., Liesaputra, V., Ramirez-Prado, G.: Tracking a person’s behaviour in a smart house. In: Liu, X., et al. (eds.) ICSOC 2018. LNCS, vol. 11434, pp. 241–252. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17642-6_21
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City (2018)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multi-task cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
Kokoulin, A.N., Tur, A.I., Yuzhakov, A.A., Knyazev, A.I.: Hierarchical convolutional neural network architecture in distributed facial recognition system. In: IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Saint Petersburg and Moscow (2019)
Teerapittayanon, S., McDanel, B., Kung, H.: Distributed deep neural networks over the cloud, the edge and end devices. In: IEEE 37th International Conference on Distributed Computing Systems (ICDCS) (2017)
Zhang, Y., Suda, N., Lai, L., Chandra, V.: Hello edge: keyword spotting on microcontrollers. arXiv preprint arXiv:1711.07128 (2017)
Lai, L., Suda, N., Chandra, V.: CMSIS-NN: efficient neural network kernels for arm cortex-M CPUs. The Computing Research Repository (2018)
George Robotics Limited: Performance, 1 June 2014. https://github.com/micropython/micropython/wiki/Performance. Accessed 1 Sept 2019
Behan, T., Liao, Z., Zhao, L.: Integer Neural Networks On Embedded Systems. In: Recent Advances in Technologies. IntechOpen (2009)
Wang, N., Choi, J., Brand, D., Chen, C.-Y., Gopalakrishnan, K.: Training deep neural networks with 8-bit floating point numbers. In: 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal (2018)
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Dokic, K., Radisic, B., Cobovic, M. (2020). MicroPython or Arduino C for ESP32 - Efficiency for Neural Network Edge Devices. In: Brito-Loeza, C., Espinosa-Romero, A., Martin-Gonzalez, A., Safi, A. (eds) Intelligent Computing Systems. ISICS 2020. Communications in Computer and Information Science, vol 1187. Springer, Cham. https://doi.org/10.1007/978-3-030-43364-2_4
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DOI: https://doi.org/10.1007/978-3-030-43364-2_4
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