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Development of an imagery representation apparatus for information representation in neyromorphic devices
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Development of an imagery representation apparatus for information representation in neyromorphic devices
Annotation
PII
S0544126924050086-1
Publication type
Article
Status
Published
Authors
N. А. Simonov 
Affiliation: Valiev Institute of Physics and Technology, Russian Academy of Sciences
Edition
Volume 53 Issue number 5
Pages
427-438
Abstract
The paper considers the application of the mathematical apparatus of spots for neuromorphic devices on crossbars of memory elements, the architecture of which corresponds to the technique of computing in memory. The apparatus of spots allows to represent and process semantic information in the form of mental imagery, as well as to model reasoning in a form inherent to a person. In particular, these are deductive, inductive, abductive, as well as non-monotonic reasoning, when conclusions are made on the basis of existing knowledge, and obtaining new knowledge can change the conclusions. The apparatus of spots is the mathematical basis for creating neuromorphic devices with the technique of computing in memory, capable of not only representing semantic information in an imaginary form, but also modeling imaginative thinking. This will solve a major problem for modern deep neural networks associated with the possibility of random, causeless errors.
Keywords
ментальные образы каузальные рассуждения нейроморфные устройства кроссбары мемристоры FeFET
Acknowledgment
The work was carried out within the framework of the State assignment of the K. A. Valiev Institute of Physics and Technology of the Russian Academy of Sciences of the Ministry of Education and Science of the Russian Federation on topic No. FFNN-2022-0019 "Fundamental and exploratory research in the field of creating a promising element base for nanoelectronics and its key technologies.
Received
23.02.2025
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References

1. Seo J.S., Brezzo B., Liu Y., Parker B.D., et al. A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons // IEEE Custom Integrated Circuits Conf (CICC). 2011. P. 1–4. IEEE.

2. Indiveri G., Linares-Barranco B., Hamilton T.J. et al. Neuromorphic silicon neuron circuits // Front. Neurosci. 2011. V. 5. P. 1–23.

3. Zhu J., Zhang T., Yang Y., and Huang R. A comprehensive review on emerging artificial neuromorphic devices // Applied Physics Reviews. 2020. Rev. 7, 011312. P. 1–107.

4. Гостев А.А. Психология вторичного образа. М.: Литрес, 2022.

5. Горбань А.Н. Проблема надежности многомерного ИИ в многомерном мире. Доклад на заседании ОНИТ РАН, 24 февраля 2022 г. URL: https://cloud.niime.ru/s/fwfX8eYHg6EDz6q?path=%2F2021%20-%202023

6. Image Classification on ImageNet. URL: https://paperswithcode.com/sota/image-classification-on-imagenet

7. Searle J.R. Minds, brains, and programs // Behav. Brain Sci. 1980. V. 3. P. 417–424.

8. Финн В.К. Индуктивные методы ДС Милля в системах искусственного интеллекта. Часть I // Искусственный интеллект и принятие решений. 2010. № 3. С. 3–21.

9. Финн В.К. Индуктивные методы ДС Милля в системах искусственного интеллекта. Часть II // Искусственный интеллект и принятие решений. 2010. № 4. С. 14–40.

10. Wang P. Cognitive Logic vs Mathematical Logic // Lecture Notes of the 3rd International Seminar on Logic and Cognition. China, Guangzhou, 2004.

11. Адамар Ж. Исследование психологии процесса изобретения в области математики. Пер. с франц. М.: МЦНМО, 2001.

12. Симонов Н.А. Концепция пятен для задач искусственного интеллекта и алгоритмов нейроморфных систем. // Микроэлектроника. 2020. Т. 49. № 6. С. 459–473.

13. Simonov N.A. Application of the model of spots for inverse problems. // Sensors. 2023. V. 23. No 3. 1247.

14. Simonov N.A., Rusalova M.N. Mental imagery representation by model of spots in psychology // Natural Systems of Mind. 2023. V. 3. No 1. P. 4–22.

15. Nanay B. Mental Imagery // E. N. Zalta (ed.) The Stanford Encyclopedia of Philosophy. 2021. [Электронный ресурс] URL: https://plato.stanford.edu/archives/win2021/entries/mental-imagery/

16. Веккер Л.М. Психика и реальность: единая теория психических процессов. М.: Cмысл, 1998.

17. Simonov N.A. Spatial representation of concepts and processes in psychology by the spots model // Natural Systems of Mind. 2024. V. 4. No 2С. 6–20. https://doi.org/ 10.38098/nsom_2024_04_02_01.

18. Sima J.F., Freksa C. Towards computational cognitive modeling of mental imagery: The attention-based quantification theory // KI-Künstliche Intelligenz. 2012. V. 26. P. 261–267.

19. Donini F.M., Lenzerini M., Nardi D., Pirri F., and Schaerf M. Nonmonotonic reasoning // Artificial Intelligence Review. 1990. V. 4. No 3. P. 163–210.

20. Светлов В.А. Методологическая концепция научного знания Чарлза Пирса: единство абдукции, дедукции и индукции // Логико-философские штудии. 2012. № 5. С. 165-188.

21. Bochman A. Causal reasoning from almost first principles // Synthese. 2024. V. 203:19.

22. Карта Карно [Электронный ресурс] // Википедия. 2023. Дата обновления: 28.10.2023. URL: https://ru.wikipedia.org/?curid=712337&oldid=133861103

23. Simonov N.A. Development of a mathematical apparatus with an imagery representation of information for neuromorphic systems. // Russian Microelectronics. 2023. V. 52. No. 6, Suppl. 1. P. S158–S161.

24. Ielmini D. and Wong H.S.P. In-memory computing with resistive switching devices // Nature electronics. 2018. V.1. No 6. P. 333–343.

25. Tarkov M., Tikhonenko F., Popov V., Antonov V., Miakonkikh A., and Rudenko K. Ferroelectric Devices for Content-Addressable Memory. Nanomaterials. 2022. V. 12. 4488.

26. Akers S.B. A rectangular logic array // 12th Annual Symposium on Switching and Automata Theory (SWAT). 1971. P. 79–90. IEEE.

27. Levy Y., Bruck J., Cassuto Y., Friedman E. G., Kolodny A., Yaakobi E., and Kvatinsky S. Logic operations in memory using a memristive Akers array // Microelectronics Journal. 2014. V. 45. No 11. P. 1429–1437.

28. Borghetti J., Snider G.S., Kuekes P.J., Yang J.J., Stewart D.R., and Williams R.S. Memristive’switches enable ‘stateful’logic operations via material implication // Nature. 2010. V. 464. P. 873–876.

29. Kvatinsky S., Belousov D., Liman S., Satat G., Wald N., Friedman E.G., Kolodny A. and Weiser U.C. MAGIC-Memristor-aided logic // IEEE Transactions on Circuits and Systems II: Express Briefs. 2014. V. 61. No 11. P. 895–899.

30. Gupta S., Imani M. and Rosing T. Felix: Fast and energy-efficient logic in memory // IEEE/ACM International Conference on Computer-Aided Design (ICCAD). 2018. P. 1–7. IEEE.

31. Удовиченко С., Писарев А., Бусыгин А., Маевский О. 3D КМОП-мемристорная нанотехнология создания логической и запоминающей матриц нейропроцессора // Наноиндустрия. 2017. № 5. С. 26–34.

32. Удовиченко С., Писарев А., Бусыгин А., Маевский О. Нейропроцессор на основе комбинированного мемристорно-диодного кроссбара // Наноиндустрия. 2018. Т. 11. № 5. С. 344–355.

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