Proceedings of MATSUS Spring 2025 Conference (MATSUSSpring25)
DOI: https://doi.org/10.29363/nanoge.matsusspring.2025.573
Publication date: 16th December 2024
The growing demand for data-driven applications calls for computing architectures going beyond traditional von Neumann architectures, where the separation between memory and processing units causes increasing latency and bandwidth bottlenecks [1]. This challenge has sparked interest in emerging ‘neuromorphic’ devices and systems, where memory and computation are seamlessly co-integrated. The brain-inspired circuits can be built from such devices by combining novel metal-oxide materials, such as HfO2 [2] and VO2 [3] that can implement synaptic and neuronal functions in the neural networks [4]. These neuro-inspired devices exhibit promising features, such as ultrafast and analog resistive switching at low energy costs. In this talk, I will present our work on VO2-based oscillator networks solving combinatorial optimization problems, including Graph Coloring, Max-cut, and Max-3SAT problems [5]. Additionally, the in-memory computing architecture of the system will be introduced by combining with HfO2-based resistive RAM (ReRAM) devices for enhanced performance and efficiency.
[1] The International Roadmap for devices and systems 2022 by IEEE
[2] “Analog resistive switching devices for DNN training with novel Tiki-Taka algorithm”, T. Stecconi, et al., T. Ando, A. Olziersky, & B. J. Offrein. Nano Letters 24, 866, 2023
[3] “Highly Reproducible and CMOS-compatible VO2-based Oscillators for Brain-inspired Computing”, O. Maher, et al. Sci Rep 14, 11600, 2024
[4] Maher, O., Jiménez, M., Delacour, C. et al. “A CMOS-compatible oscillation-based VO2 Ising machine solver” Nat Commun 15, 3334 (2024). https://doi.org/10.1038/s41467-024-47642-5
[5] G. Csaba and W. Porod, "Coupled oscillators for computing: A review and perspective," Applied Physics Review, 3 January 2020
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