K.V. Selivanov1, V.S. Klimachev2

1–2 Department IU4 of Designing and Technology of Electronic Equipment, Bauman Moscow State Technical University (Moscow, Russia)

The development of software tools for electronic equipment has led to the development and widespread use of neural network technology. They are used for processing and making decisions based on the received information, which is not discrete, but has a polymorphic essence. Processing entity data and computing decisions requires significant amounts of computing power and device operating memory. This problem does not allow the widespread use of neural network technologies in portable devices and devices based on microcontrollers.

The aim of the article – adapt neural network technology for use on portable environments and microcontroller-based electronic devices.

The chosen method of implementing a neural network based on the resource-saving Hamming algorithm, and the optimized program code in the C language made it possible to significantly reduce the requirements for the hardware of the device on which this technology can be implemented. The analysis of modern microcontrollers allowed us to choose and apply the optimal power-to-energysaving microcontroller-STM32, which allowed us to implement a simplified neural network on its basis.

The developed algorithm was implemented on a debug board with an STM32 microcontroller in a device that allows you to recognize handwritten numbers entered from the touch screen. The created portable device for recognizing handwritten numbers is applicable as a module in other electronic equipment products. The prospects of using the latest variants of implementing neurocomputers on microcontrollers are shown.

Selivanov K.V. Klimachev V.S. System design of neurocomputers on microcontrollers in conditions of limited resources. Neurocomputers. 2021. V. 23. № 3. Р. 35−42. DOI: https://doi.org/10.18127/j19998554-202103-04 (in Russian).

1. Yusupova N.I., Smetanina O.N., Gayanova M.M. Tekhnologii iskusstvennogo intellekta i mashinnogo obucheniya v zadachakh semanticheskogo predstavleniya i analiza dannykh. M.: Innovatsionnoye mashinostroyeniye. 2020. 241 s (in Russian).
2. Yurkov N.K. Mashinnyy intellekt i obucheniye cheloveka: monografiya. Penza: PGU. 2008. 224 s (in Russian).
3. Aminev D.A., Danilevich V.A., Demin A.A., Samman A. Raspoznavaniye rukopisnykh simvolov na sensornoy matritse s ispolzovaniyem neyronnoy seti. Neyrokompyutery: razrabotka. primeneniye. 2021. T. 23. № 1. S. 32–42 (in Russian).
4. Demin A.A., Vlasov A.I., Shakhnov V.A. Metody i sredstva vizualnykh uprazhneniy dlya adaptivnoy korrektsii tonkoy motoriki kistey ruk v usloviyakh nevesomosti. Vestnik Moskovskogo gosudarstvennogo tekhnicheskogo universiteta im. N.E. Baumana. Seriya Priborostroyeniye. 2015. № 3 (102). S. 23–38 (in Russian).
5. Demin A.A. Metody avtomatizirovannoy otsenki kalligrafii. Programmnyye produkty i sistemy. 2011. № 1. S. 20–23 (in Russian).
6. Romanyuk A.G. Ispolzovaniye glubokogo obucheniya neyroseti dlya raspoznavaniya golosovykh komand polzovatelya. Zhurnal radioelektroniki. 2019. S. 10–20 (in Russian).
7. Chuchalin A.G., Chereshnev V.A. Bioetika. iskusstvennyy intellekt i meditsinskaya diagnostika. V kn: Posvyashchayetsya 150-letiyu otkrytiya Periodicheskogo zakona khimicheskikh elementov. Perm: Permskiy gosudarstvennyy meditsinskiy universitet im. Ak. E.A. Vagnera. 2019. 208 s (in Russian).
8. Khomutov R.A., Feshina E.V. Iskusstvennyy intellekt v meditsine – perspektivy razvitiya. Sb. materialov I vseros. studench. nauchnopraktich. konf. Krasnodar: Kubanskiy gosudarstvennyy agrarnyy universitet im. I.T. Trubilina. 2019 S. 234–236 (in Russian).
9. Ivanov E.S. Informatsionnyye tekhnologii. Pyatigorsk: RIA-KMV. 2017. 847 s (in Russian).
10. Averianikhin A.E., Vlasov A.I., Evdokimova E.V. Ierarkhicheskaya piramidalnaya subdiskretizatsiya v glubokikh svertochnykh setyakh dlya raspoznavaniya vizualnykh obrazov. Neyrokompyutery: razrabotka. primeneniye. 2021. T. 23. № 1. S. 17–31 (in Russian).
11. Viryasova A.Yu., Vlasov A.I., Gladkikh A.A. Neyrosetevyye metody defektoskopii integralnykh struktur. Neyrokompyutery: razrabotka. primeneniye. 2019. № 2. S. 54–67 (in Russian).
12. Vlasov A.I., Papulin S.Yu. Analiz dannykh s ispolzovaniyem gistogrammnoy modeli kombinatsii priznakov. Neyrokompyutery: razrabotka. primeneniye. 2019. T. 21. № 5. S. 18–27 (in Russian).
13. Vlasov A.I., Zakharov E.R., Zakharova V.O. Neyrosetevaya sistema obnaruzheniya i neytralizatsii udalennogo nesanktsionirovannogo vmeshatelstva v komponenty interneta veshchey. Neyrokompyutery: razrabotka. primeneniye. 2021. T. 23. № 1. S. 63–80 (in Russian).
14. Zaytsev D.E. Iskusstvennyy intellekt v informatsionnykh sistemakh. Informatsionnyye tekhnologii v sovremennom mire. 2019. S. 113–115 (in Russian). Neurocomputers/Neirokompiutery V. 23, № 3, 2021, P. 35−42
15. Krotov A.D., Petrov A.A. Ispolzovaniye iskusstvennogo intellekta v selskom khozyaystve. Informatsionnoye obshchestvo: sovremennoye sostoyaniye i perspektivy razvitiya. 2020. S. 153–156 (in Russian).
16. Ning J. Derong L. Wavelet Basis Function Neural Networks for Sequential Learning. IEEE Transactions on Neural Networks. 2008. V. 19. Iss. 3. P. 523–528. doi: 10.1109/TNN.2007.911749
17. Kouhara T., Okabe Y. Learning algorithm based on moderationism for multi-layer neural networks. Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan). doi: 10.1109/IJCNN.1993.713960
18. Vosmirko S.O. Razrabotka matematicheskogo i programmnogo obespecheniya sredy modelirovaniya neyronnykh setey dlya resheniya zadach prognozirovaniya: dissertatsiya.,. kand. tekhn. nauk. M.: 2004. 158 s (in Russian).
19. Margrave F., Babu N.R., Bradshaw A., Collins I. MATLAB-neural networks toolbox hardware post-processor. IEE Colloquium on Applied Control Techniques Using MATLAB. 2002. doi: 10.1049/ic:19950057
20. Soldatova O.P. Osnovy neyroinformatiki: uchebnoye posobiye. Samara: Izd-vo SGAU. 2006. 131 s (in Russian).
21. Shamsul A.Z. Nurrul A.Z. Fathin J.B. Mohamad H.H. Applications on STM32 Nucleo Microcontroller for Pulse Switching Using Time Delay and Trigonometric Function with MATLAB Simulink. 2020 IEEE International Conference on Power and Energy (PECon). 2021. doi: 10.1109/PECon48942.2020.9314509
22. Patrik J., Dobroslav K., Radoslav B., Tibor V., Oleksii K. 2017 International Conference on Modern Electrical and Energy Systems (MEES). 2017. Kremenchuk, Ukraine. doi: 10.1109/MEES.2017.8248906