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Memcomputing Numerical Inversion With Self-Organizing Logic Gates

delete2018-06-01
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H
Haik Manukian *
F
Fabio L. Traversa
M
Massimiliano Di Ventra
DOI:10.1109/TNNLS.2017.2697386delete
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Abstract

Abstract

En 中文
We propose to use digital memcomputing machines (DMMs), implemented with self-organizing logic gates (SOLGs), to solve the problem of numerical inversion. Starting from fixed-point scalar inversion, we describe the generalization to solving linear systems and matrix inversion. This method, when realized in hardware, will output the result in only one computational step. As an example, we perform simulations of the scalar case using a 5-bit logic circuit made of SOLGs, and show that the circuit successfully performs the inversion. Our method can be extended efficiently to any level of precision, since we prove that producing n-bit precision in the output requires extending the circuit by at most n bits. This type of numerical inversion can be implemented by DMM units in hardware; it is scalable, and thus of great benefit to any real-time computing application.
Keywords:
Emerging technologies
memcomputing
numerical linear algebra
self-organizing systems
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Journal

IEEE Transactions on Neural Networks and Learning Systems cover
IEEE Transactions on Neural Networks and Learning Systems
IF:
8.9
Papers:
7.5K
Citations:
7.2W

Organization

University of California System cover
University of California System
Scholars:
37.5W
Papers: 33.7W
Citations: 6.6K
U
University of California San Diego
Scholars:
4.6W
Papers: 3.5W
Citations: 924
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