In this paper we propose a new mem-computing image processing architecture, called Memristor Cellular Nonlinear Network, which leverages the unique capability of nonvolatile memristors to compute and store data in the same physical nano-scale locations. Adopting a bistable-like memristor in place for the linear resistor in the standard realization of a cell of the nonlinear dynamic array, the resulting network is capable to process information by exploiting the time evolution of the voltages across the memristors as well as to store/retrieve results into/ from the memristances. This attractive feature, absent in a standard Cellular Nonlinear Network, may pave the way towards the future development of a new generation of visual processors with unprecedented spatial resolution.

Mem-computing CNNs with bistable-like memristors / Messaris, I; Ascoli, A; Meinhardt, Gs; Tetzlaff, R; Chua, Lo. - STAMPA. - (2019). (Intervento presentato al convegno IEEE International Symposium on Circuits and Systems tenutosi a Sapporo (Japan) nel 26-29 May 2019) [10.1109/ISCAS.2019.8702414].

Mem-computing CNNs with bistable-like memristors

Ascoli A;
2019

Abstract

In this paper we propose a new mem-computing image processing architecture, called Memristor Cellular Nonlinear Network, which leverages the unique capability of nonvolatile memristors to compute and store data in the same physical nano-scale locations. Adopting a bistable-like memristor in place for the linear resistor in the standard realization of a cell of the nonlinear dynamic array, the resulting network is capable to process information by exploiting the time evolution of the voltages across the memristors as well as to store/retrieve results into/ from the memristances. This attractive feature, absent in a standard Cellular Nonlinear Network, may pave the way towards the future development of a new generation of visual processors with unprecedented spatial resolution.
2019
978-1-7281-0397-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2988487