Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Radu Berdan"'
Publikováno v:
Nature Communications, Vol 7, Iss 1, Pp 1-9 (2016)
Artificial neural networks exhibit learning abilities and can perform tasks which are tricky for conventional computing systems, such as pattern recognition. Here, Serb et al. show experimentally that memristor arrays can learn reversibly from noisy
Externí odkaz:
https://doaj.org/article/cdeb3fafed714e98959c6868368644df
Autor:
Kensuke Ota, Radu Berdan, Jun Deguchi, Masumi Saitoh, Takao Marukame, Yoshifumi Nishi, Marina Yamaguchi, Shosuke Fujii
Publikováno v:
Nature Electronics. 3:259-266
Analogue in-memory computing using memristors could alleviate the performance constraints imposed by digital von Neumann systems in data-intensive tasks. Conventional linear memristors typically operate at high currents, potentially limiting power ef
Publikováno v:
IEEE Electron Device Letters. 38:28-31
Emerging technologies, such as resistive random access memory (RRAM), are being actively researched for its potential applications in developing new technologies inspired by brainlike neuromorphic computing. However, developing automated characteriza
Autor:
Masumi Saitoh, Shosuke Fujii, Jun Deguchi, Takao Marukame, Radu Berdan, Yoshifumi Nishi, Shoichi Kabuyanagi, Kensuke Ota
Publikováno v:
2019 Symposium on VLSI Technology.
Building compact and efficient reinforcement learning (RL) systems for mobile deployment requires departure from the von-Neumann computing architecture and embracing novel in-memory computing, and local learning paradigms. We exploit nano-scale ferro
Autor:
Yoshifumi Nishi, Yutaka Tamura, Kumiko Nomura, Kazuo Ishikawa, Koji Takahashi, Radu Berdan, Junichi Sugino, Takao Marukame, Toshimitsu Kitamura
Publikováno v:
ISCAS
A low-power and stable “static-type” neural network (NN) circuit based on CMOS and resistive synaptic devices was developed and evaluated. The circuit is composed of a comparator as a firing function for binary output, current sources, cross swit
Publikováno v:
IEEE Transactions on Circuits and Systems II: Express Briefs. 62:676-680
Devices that exhibit resistive switching are promising components for future nanoelectronics with applications ranging from emerging memory to neuromorphic computing and biosensors. In this brief, we present an algorithm for identifying switchable de
Autor:
Radu Berdan, Anna Regoutz, Ali Khiat, Alexander Serb, Themis Prodromakis, Christos Papavassiliou
Publikováno v:
IEEE Transactions on Electron Devices. 62:2190-2196
Selectorless crossbar arrays of resistive random-access memory (RRAM), also known as memristors, conduct large sneak currents during operation, which can significantly corrupt the accuracy of cross-point analog resistance ( $M_{t}$ ) measurements. In
Publikováno v:
ISCAS
A flexible, versatile, and visually rich memristor crossbar simulator is presented in this paper. The system is represented by a Python graphical user interface (GUI) and memristor simulator engine which can instantiate crossbars of any size made out
An FPGA-based instrument with capabilities of on-board oscilloscope and nanoscale pulsing (70 ns @ $\pm$ 10 V) is presented, thus allowing exploration of the nano-scale switching of RRAM devices. The system possesses less than 1% read-out error for r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::713cae5381b50bb7abe53c26658838d3
https://eprints.soton.ac.uk/397927/
https://eprints.soton.ac.uk/397927/
Publikováno v:
ISCAS
2016 IEEE International Symposium on Circuits and Systems (ISCAS)
2016 IEEE International Symposium on Circuits and Systems (ISCAS)
We demonstrate a desktop platform which has the ability of fully characterizing RRAM crossbar arrays while not compromising on ease-of-use. The setup consists of our bespoke PCB system connected to a local PC (laptop), on which a Pyhton interface all