Strategic Investment in the Research and Development of Memristor Technology in the Republic of Serbia
Abstract
The rapid advancement of Artificial Intelligence (AI) has significantly impacted both high technology development and economic and social progress. The Republic of Serbia has been strategically supporting research and development of in the field of AI. Given the dramatic dynamic development of AI, the aim of this paper is to identify and describe memristor technology as currently very relevant and attractive, in order to achieve technological innovation, socio-economic benefits, and potentially global breakthroughs. The paper presents an overview of literature to analyze theoretical concepts, current research outcomes in AI, and possible applications of memristors. The analyses indicate that adoption and development of memristor technology in Serbia can position the country as a leader in AI hardware innovation, attracting international partners and fostering a technologically advanced industrial system. Therefore, this paper suggests that future research should focus on overcoming practical challenges in the production of memristors, developing hybrid architectures, and formulating advanced neuromorphic algorithms.
References
Adhikari, S. P. et al. (2012). Memristor bridge synapse-based neural network and its learning. IEEE Transactions on Neural Networks and Learning Systems, XXIII (9), 1426–1435. https://doi.org/10.1109/TNNLS.2012.2204770
Ambrogio, S. et al. (2018). Equivalent-accuracy accelerated neural-network training using analogue memory. Nature, DLVIII (7708), 60–67. https://doi.org/10.1038/s41586-018-0180-5
Auto (2023). New steps in the development of the legal framework for autonomous driving. Available at: http://www.ai.gov.rs/vest/sr/771/novi-koraci-u-razvoju-pravnog-okvira-za-autonomnu-voznju.php
Bojic, L. (2022). Metaverse through the prism of power and addiction: What will happen when the virtual world becomes more attractive than reality? European Journal of Futures Research, X (1), 22.https://doi.org/10.1186/s40309-022-00208-4
Bojic, L. (2024). AI alignment: Assessing the global impact of recommender systems. Futures, CLX, 103383. https://doi.org/10.1016/j.futures.2024.103383
Bojic, L. et al. (2024a). AI and Energy Consumption: Social Aspects, 1-4. DOI: 10.23919/SpliTech61897.2024.10612493
Chang, T., Jo, S.-H. & Lu, W. (2011). Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano, V (9), 7669–7676. https://doi.org/10.1021/nn202983n
Chua, L. (1971). Memristor-The missing circuit element. IEEE Transactions on Circuit Theory, XVIII (5), 507–519. https://doi.org/10.1109/TCT.1971.1083337
Etika (2023). Adopted ethical guidelines for the development and use of artificial intelligence. Available at: https://www.srbija.gov.rs/vest/692988/usvojene-eticke-smernice-za-razvoj-i-upotrebu-vestacke-inteligencije.php
IVI (2022). Artificial Intelligence Research and Development Institutes. Available at: https://ivi.ac.rs/
Prezioso, M. et al. (2015). Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, DXXI (7550), 61–64. https://doi.org/10.1038/nature14441
Strategy (2020). Strategy for the Development of Artificial Intelligence. Available at: https://www.srbija.gov.rs/tekst/437277/strategija-razvoja-vestacke-inteligencije.php
Strukov, D. B. et al. (2008). The missing memristor found. Nature, CDLIII (7191), 80–83. https://doi.org/10.1038/nature06932
Talanov, M., Vallverdu, J., Bojic, L. (2024b). Neuropunk revolution: memristive spinal CPG learning approach. 9th International Conference on Smart and Sustainable Technologies (SPLITECH), June 25–28, 2024, Split, Croatia.
Wang, Z. et al. (2017). Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nature Materials, XVI (1), 101–108. https://doi.org/10.1038/nmat4756
Yang, J. J., Strukov, D. B., Stewart, D. R. (2013). Memristive devices for computing. Nature Nanotechnology, VIII (1), 13–24, https://doi.org/10.1038/nnano.2012.240
