K. K. Abgaryan
Papers
2
Total Citations
4
H-Index
2
About
K. K. Abgaryan is a researcher focused on the mathematical modeling of neuromorphic computing systems, particularly those based on nanosized memristive elements. Their key research area lies at the intersection of artificial neural networks and hardware implementation, addressing the high computational costs that have historically limited the widespread deployment of neural networks in tasks such as image and speech recognition, robotics, and unmanned systems. Abgaryan’s major contribution is the development of a mathematical framework for self-learning neuromorphic networks using a 1T1R crossbar architecture, which promises more efficient, hardware-based learning. This work, published in 2020 and 2021, has garnered 2 citations each, reflecting its niche but foundational impact in the emerging field of memristive computing. By tackling the bottleneck of computational expense, Abgaryan’s research paves the way for more practical, low-power neural network implementations, making it a notable step toward advanced neuromorphic hardware.
Research Focus
Key Achievements
Top Papers
- 1
- 2