Abstract
In nonlinear dynamical systems with barriers/thresholds, the signal response against a weak external input signal is enhanced by an appropriate additive noise (stochastic resonance). In recent years, progress in the application of stochastic resonance shows that the existence of additive noise heightens the memory storage functions in memory elements using bistable oscillations even with extremely low power consumption. By not restricting the additive noise, the deterministic chaos (an internal fluctuation) induces a similar phenomenon known as chaotic resonance. Chaotic resonance appears in nonlinear dynamical systems and is accompanied by chaos–chaos intermittency, where the chaotic orbit intermittently transitions among separated attractor regions through attractor-merging bifurcation. Previously, a higher chaotic resonance sensitivity than that of stochastic resonance was reported in various types of systems. In this study, we hypothesize that chaotic-resonance-based memory devices can store information with lower power consumption than that of stochastic-resonance-based devices. To prove this hypothesis, we induced attractor-merging bifurcation in a cubic map system, which is the simplest model for the emergence of chaotic resonance. Thereafter, we adjusted the internal system parameter under a noise-free system as the chaotic resonance and applied stochastic noise similar to the condition for inducing stochastic resonance. The results of this study reveal that, even with weaker memory storage input signals, the former exhibits a higher memory storage capability than the latter. The approach using chaotic resonance could facilitate the development of memory devices that were hitherto restricted to the application of stochastic resonance.
Highlights
Considering recent developments in artificial intelligence (AI), neuromorphic computing, and big data analysis, the amount of data being stored is increasing exponentially [1]
DEPENDENCY OF SYSTEM BEHAVIOR ON THE INTERNAL PARAMETER OF THE CUBIC MAP First, we demonstrate the dependency of system behavior on the internal parameter of cubic map a
This paper proposed a mechanism for memory storage using the high sensitivity of the signal response around the attractor-merging bifurcation in the assembly of cubic maps with chaos–chaos intermittency and evaluated its capability
Summary
Considering recent developments in artificial intelligence (AI), neuromorphic computing, and big data analysis, the amount of data being stored is increasing exponentially [1]. To manage such large amounts of data, the development of a data storage device with high density and high speed data transmission is being currently undertaken [2]. Memory storage devices with low power consumption are needed. To develop such a device, the stochastic resonance mechanism [10] (reviewed in [11]–[13]), in which synchronization in nonlinear systems possessing a barrier/threshold under a
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