Zobrazeno 1 - 10
of 770
pro vyhledávání: '"Menzel, Stephan"'
Autor:
Ascoli, Alon, Menzel, Stephan, Rana, Vikas, Kempen, Tim, Messaris, Ioannis, Demirkol, Ahmet Samil, Schulten, Michael, Siemon, Anne, Tetzlaff, Ronald
The multidisciplinary field of memristors calls for the necessity for theoreticallyinclined researchers and experimenters to join forces, merging complementary expertise and technical know-how, to develop and implement rigorous and systematic techniq
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A88339
https://tud.qucosa.de/api/qucosa%3A88339/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A88339/attachment/ATT-0/
Autor:
Singh, Chandan, Ntinas, Vasileios, Prousalis, Dimitrios, Wang, Yongmin, Demirkol, Ahmet Samil, Messaris, Ioannis, Rana, Vikas, Menzel, Stephan, Ascoli, Alon, Tetzlaff, Ronald
This paper introduces an innovative graphical analysis tool for investigating the dynamics of Memristor Cellular Nonlinear Networks (M-CNNs) featuring 2nd-order processing elements, known as M-CNN cells. In the era of specialized hardware catering to
Externí odkaz:
http://arxiv.org/abs/2408.03260
Autor:
Hennen, Tyler, Brackmann, Leon, Ziegler, Tobias, Siegel, Sebastian, Menzel, Stephan, Waser, Rainer, Wouters, Dirk J., Bedau, Daniel
We present a fast generative modeling approach for resistive memories that reproduces the complex statistical properties of real-world devices. To enable efficient modeling of analog circuits, the model is implemented in Verilog-A. By training on ext
Externí odkaz:
http://arxiv.org/abs/2404.06344
Publikováno v:
56th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2022, pp. 1142-1146
Simulation frameworks such MemTorch, DNN+NeuroSim, and aihwkit are commonly used to facilitate the end-to-end co-design of memristive machine learning (ML) accelerators. These simulators can take device nonidealities into account and are integrated w
Externí odkaz:
http://arxiv.org/abs/2403.06746
Autor:
Sarantopoulos, Alexandros, Lange, Kristof, Rivadulla, Francisco, Menzel, Stephan, Dittmann, Regina
Enhancing the switching speed of oxide-based memristive devices at a low voltage level is crucial for their use as non-volatile memory and their integration into emerging computing paradigms such as neuromorphic computing. Efforts to accelerate the s
Externí odkaz:
http://arxiv.org/abs/2402.07603
Autor:
Völkel, Lukas, Braun, Dennis, Belete, Melkamu, Kataria, Satender, Wahlbrink, Thorsten, Ran, Ke, Kistermann, Kevin, Mayer, Joachim, Menzel, Stephan, Daus, Alwin, Lemme, Max C.
Publikováno v:
Advanced Functional Materials, 202300428, 2023
The two-dimensional (2D) insulating material hexagonal boron nitride (h BN) has attracted much attention as the active medium in memristive devices due to its favorable physical properties, among others, a wide bandgap that enables a large switching
Externí odkaz:
http://arxiv.org/abs/2301.10158
Autor:
Stephanou, Coralea1 (AUTHOR) coraleas@cing.ac.cy, Menzel, Stephan2 (AUTHOR), Philipsen, Sjaak3 (AUTHOR), Kountouris, Petros1 (AUTHOR)
Publikováno v:
International Journal of Molecular Sciences. Nov2024, Vol. 25 Issue 21, p11408. 26p.
Autor:
Staudigl, Felix, Indari, Hazem Al, Schön, Daniel, Sisejkovic, Dominik, Merchant, Farhad, Joseph, Jan Moritz, Rana, Vikas, Menzel, Stephan, Leupers, Rainer
Emerging non-volatile memory (NVM) technologies offer unique advantages in energy efficiency, latency, and features such as computing-in-memory. Consequently, emerging NVM technologies are considered an ideal substrate for computation and storage in
Externí odkaz:
http://arxiv.org/abs/2112.01087