Memristor as an archetype of dynamic data-driven systems and applications to sensor networks


Giovanni E. Pazienza and Robert Kozma

Center for Large-Scale Integration, Optimization, and Networks CLION, Department of Mathematical Sciences, University of Memphis, TN {g.pazienza,rkozma}@memphis.edu. GE Pazienza is also with Pazmany University, Budapest.


Abstract


Since its introduction a decade ago, DDDAS has been applied to a wide range of science and technology fields, with specific focus of areas requiring fast and reliable processing of massive data streams from diverse resources. It is crucial to explore architectures and systems which are naturally suited to the DDDAS framework. In this paper, we show that the memristor – the fourth fundamental two-terminal passive circuit element alongside the well-known resistor, capacitor, and inductor – affords the efficient implementation of the working principles of DDDAS. Hence memristors can be considered as an archetype of DDDAS nanoscale hardware embodiment, being the smallest and most basic dynamic data driven application system. Memristors are electrical components with inherent memory processes; they have been predicted about four decade ago and have been physically implemented recently. We discuss the role that DDDAS may play in the development of computing platforms and sensor networks based on memristors in the next few years.


Keywords


DDDAS,Memristor,SensorNetworks