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29 results on '"Ambrosi, Elia"'

1. In-memory eigenvector computation in time O(1)

Author

Sun, Zhong, Pedretti, Giacomo, Ambrosi, Elia, Bricalli, Alessandro, and Ielmini, Daniele

Subjects
Computer Science - Computational Complexity, Computer Science - Emerging Technologies, Mathematics - Numerical Analysis
Abstract

In-memory computing with crosspoint resistive memory arrays has gained enormous attention to accelerate the matrix-vector multiplication in the computation of data-centric applications. By combining a crosspoint array and feedback amplifiers, it is possible to compute matrix eigenvectors in one step without algorithmic iterations. In this work, time complexity of the eigenvector computation is investigated, based on the feedback analysis of the crosspoint circuit. The results show that the computing time of the circuit is determined by the mismatch degree of the eigenvalues implemented in the circuit, which controls the rising speed of output voltages. For a dataset of random matrices, the time for computing the dominant eigenvector in the circuit is constant for various matrix sizes, namely the time complexity is O(1). The O(1) time complexity is also supported by simulations of PageRank of real-world datasets. This work paves the way for fast, energy-efficient accelerators for eigenvector computation in a wide range of practical applications., Comment: Accepted by Adv. Intell. Syst

Published
2020
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2. Time complexity of in-memory solution of linear systems

Author

Sun, Zhong, Pedretti, Giacomo, Mannocci, Piergiulio, Ambrosi, Elia, Bricalli, Alessandro, and Ielmini, Daniele

Subjects
Computer Science - Computational Complexity, Computer Science - Emerging Technologies, Mathematics - Numerical Analysis
Abstract

In-memory computing with crosspoint resistive memory arrays has been shown to accelerate data-centric computations such as the training and inference of deep neural networks, thanks to the high parallelism endowed by physical rules in the electrical circuits. By connecting crosspoint arrays with negative feedback amplifiers, it is possible to solve linear algebraic problems such as linear systems and matrix eigenvectors in just one step. Based on the theory of feedback circuits, we study the dynamics of the solution of linear systems within a memory array, showing that the time complexity of the solution is free of any direct dependence on the problem size N, rather it is governed by the minimal eigenvalue of an associated matrix of the coefficient matrix. We show that, when the linear system is modeled by a covariance matrix, the time complexity is O(logN) or O(1). In the case of sparse positive-definite linear systems, the time complexity is solely determined by the minimal eigenvalue of the coefficient matrix. These results demonstrate the high speed of the circuit for solving linear systems in a wide range of applications, thus supporting in-memory computing as a strong candidate for future big data and machine learning accelerators., Comment: Accepted by IEEE Trans. Electron Devices. The authors thank Scott Aaronson for helpful discussion about time complexity

Published
2020
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5. Forming-Free Selectors Based on Te in an Insulating SiO xMatrix

Author

Datye, Isha M., primary, Vaziri, Sam, additional, Ambrosi, Elia, additional, Khan, Asir Intisar, additional, Kwon, Heungdong, additional, Wu, Cheng-Hsien, additional, Hsu, Chen-Feng, additional, Guy, Jeremy, additional, Lee, Tung-Ying, additional, Wong, H.-S. Philip, additional, and Bao, Xinyu, additional

Published
2024
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8. Forming-Free Selectors Based on Te in an Insulating SiOx Matrix

Author

Datye, Isha M., Vaziri, Sam, Ambrosi, Elia, Khan, Asir Intisar, Kwon, Heungdong, Wu, Cheng-Hsien, Hsu, Chen-Feng, Guy, Jeremy, Lee, Tung-Ying, Wong, H.-S. Philip, and Bao, Xinyu

Abstract

Low voltage and low OFF-current selectors are needed to meet the requirements for cross-point array architectures in embedded memories. Chalcogenide-based selectors have been widely studied for such applications, but no single material system has yet demonstrated all the specifications required for integration with nonvolatile memory. This article presents arsenic-free selectors based on a simple and stable material system consisting of Te in an insulating SiOx matrix. By modifying the thickness and atomic composition of films, tunable switching voltage and OFF-current are achieved. Optimized forming-free SiOxTey selectors are demonstrated with a threshold voltage of ~1.2 V and an OFF-current of ~3 nA at 0.5 V. Size- and temperature-dependent measurements provide insight into the different conduction mechanisms of devices with different forming behaviors. Threshold voltage drift of ~30 mV/decade is measured for both forming-required and forming-free devices. These selectors maintain their functionality after a 300°C anneal and a recovery pulse, and they are stable after one year stored in air. Excellent endurance of 1011 cycles is reported, making these devices promising for integration with nonvolatile memory.

Published
2024
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18. Electrochemical metallization ReRAMs (ECM) - Experiments and modelling: general discussion

Author

Ambrosi, Elia, Bartlett, Philip, Berg, Sanne, Brivio, Stefano, Burr, Geoffrey, Deswal, Sweety, Deuermeier, Jonas, Haga, Masa-aki, Kiazadeh, Asal, Kissling, Gabriela, Kozicki, Michael, Foroutan-Nejad, Cina, Gale, Ella, Gonzalez-Velo, Yago, Goossens, Anouk, Goux, Ludovic, Hasegawa, Tsuyoshi, Hilgenkamp, Hans, Huang, Ruomeng, Ibrahim, Sherif, Ielmini, Daniele, Kenyon, Tony, Kolosov, Vladimir, Li, Yang, Majumdar, Sayani, Milano, Gianluca, Prodromakis, Themistoklis, Raeishosseini, Niloufar, Rana, Vikas, Ricciardi, Carlo, Santamaria, Monica, Shluger, Alexander, Valov, Ilia, Waser, Rainer, Williams, Stanley, Wouters, Dirk, Yang, Yuchao, Zaffora, Andrea, Ambrosi, Elia, Bartlett, Philip, Berg, Sanne, Brivio, Stefano, Burr, Geoffrey, Deswal, Sweety, Deuermeier, Jonas, Haga, Masa-aki, Kiazadeh, Asal, Kissling, Gabriela, Kozicki, Michael, Foroutan-Nejad, Cina, Gale, Ella, Gonzalez-Velo, Yago, Goossens, Anouk, Goux, Ludovic, Hasegawa, Tsuyoshi, Hilgenkamp, Hans, Huang, Ruomeng, Ibrahim, Sherif, Ielmini, Daniele, Kenyon, Tony, Kolosov, Vladimir, Li, Yang, Majumdar, Sayani, Milano, Gianluca, Prodromakis, Themistoklis, Raeishosseini, Niloufar, Rana, Vikas, Ricciardi, Carlo, Santamaria, Monica, Shluger, Alexander, Valov, Ilia, Waser, Rainer, Williams, Stanley, Wouters, Dirk, Yang, Yuchao, and Zaffora, Andrea

Published
2019

21. Electrochemical metallization ReRAMs (ECM) - Experiments and modelling: general discussion

Author

Ambrosi, Elia, primary, Bartlett, Philip, additional, Berg, Alexandra I., additional, Brivio, Stefano, additional, Burr, Geoffrey, additional, Deswal, Sweety, additional, Deuermeier, Jonas, additional, Haga, Masa-aki, additional, Kiazadeh, Asal, additional, Kissling, Gabriela, additional, Kozicki, Michael, additional, Foroutan-Nejad, Cina, additional, Gale, Ella, additional, Gonzalez-Velo, Yago, additional, Goossens, Anouk, additional, Goux, Ludovic, additional, Hasegawa, Tsuyoshi, additional, Hilgenkamp, Hans, additional, Huang, Ruomeng, additional, Ibrahim, Sherif, additional, Ielmini, Daniele, additional, Kenyon, Anthony J., additional, Kolosov, Vladimir, additional, Li, Yang, additional, Majumdar, Sayani, additional, Milano, Gianluca, additional, Prodromakis, Themis, additional, Raeishosseini, Niloufar, additional, Rana, Vikas, additional, Ricciardi, Carlo, additional, Santamaria, Monica, additional, Shluger, Alexander, additional, Valov, Ilia, additional, Waser, Rainer, additional, Williams, R. Stanley, additional, Wouters, Dirk, additional, Yang, Yuchao, additional, and Zaffora, Andrea, additional

Published
2019
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24. Silicon Oxide (SiOx): A Promising Material for Resistance Switching?

Author

Mehonic, Adnan, primary, Shluger, Alexander L., additional, Gao, David, additional, Valov, Ilia, additional, Miranda, Enrique, additional, Ielmini, Daniele, additional, Bricalli, Alessandro, additional, Ambrosi, Elia, additional, Li, Can, additional, Yang, J. Joshua, additional, Xia, Qiangfei, additional, and Kenyon, Anthony J., additional

Published
2018
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27. Solving matrix equations in one step with cross-point resistive arrays.

Author

Zhong Sun, Pedretti, Giacomo, Ambrosi, Elia, Bricalli, Alessandro, Wei Wang, and Ielmini, Daniele

Subjects
BOOLEAN functions, LINEAR systems, LINEAR equations, KIRCHHOFF'S theory of diffraction, OHM'S law, SCHRODINGER equation
Abstract

Conventional digital computers can execute advanced operations by a sequence of elementary Boolean functions of 2 or more bits. As a result, complicated tasks such as solving a linear system or solving a differential equation require a large number of computing steps and an extensive use of memory units to store individual bits. To accelerate the execution of such advanced tasks, inmemory computing with resistive memories provides a promising avenue, thanks to analog data storage and physical computation in the memory. Here, we show that a cross-point array of resistive memory devices can directly solve a system of linear equations, or find the matrix eigenvectors. These operations are completed in just one single step, thanks to the physical computing with Ohm's and Kirchhoff's laws, and thanks to the negative feedback connection in the cross-point circuit. Algebraic problems are demonstrated in hardware and applied to classical computing tasks, such as ranking webpages and solving the Schrödinger equation in one step. [ABSTRACT FROM AUTHOR]

Published
2019
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29. Silicon Oxide (SiO x ): A Promising Material for Resistance Switching?

Author

Mehonic A, Shluger AL, Gao D, Valov I, Miranda E, Ielmini D, Bricalli A, Ambrosi E, Li C, Yang JJ, Xia Q, and Kenyon AJ

Abstract

Interest in resistance switching is currently growing apace. The promise of novel high-density, low-power, high-speed nonvolatile memory devices is appealing enough, but beyond that there are exciting future possibilities for applications in hardware acceleration for machine learning and artificial intelligence, and for neuromorphic computing. A very wide range of material systems exhibit resistance switching, a number of which-primarily transition metal oxides-are currently being investigated as complementary metal-oxide-semiconductor (CMOS)-compatible technologies. Here, the case is made for silicon oxide, perhaps the most CMOS-compatible dielectric, yet one that has had comparatively little attention as a resistance-switching material. Herein, a taxonomy of switching mechanisms in silicon oxide is presented, and the current state of the art in modeling, understanding fundamental switching mechanisms, and exciting device applications is summarized. In conclusion, silicon oxide is an excellent choice for resistance-switching technologies, offering a number of compelling advantages over competing material systems., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Published
2018
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