ﻻ يوجد ملخص باللغة العربية
The feasibility of reservoir computing based on dipole-coupled nanomagnets is demonstrated using micro-magnetic simulations. The reservoir consists of an 2x10 array of nanomagnets. The static-magnetization directions of the nanomagnets are used as reservoir states. To update these states, we change the magnetization of one nanomagnet according to a single-bit-sequential signal. We also change the uniaxial anisotropy of the other nanomagnets using a voltage-induced magnetic-anisotropy change to enhance information flow, storage, and linear/nonlinear calculations. Binary tasks with AND, OR, and XOR operations were performed to evaluate the performance of the magnetic-array reservoir. The reservoir-computing output matrix was found to be trainable to perform AND, OR, and XOR operations with an input delay of up to three bits.
This work describes preliminary steps towards nano-scale reservoir computing using quantum dots. Our research has focused on the development of an accumulator-based sensing system that reacts to changes in the environment, as well as the development
Reservoir computing (RC) offers efficient temporal data processing with a low training cost by separating recurrent neural networks into a fixed network with recurrent connections and a trainable linear network. The quality of the fixed network, call
We simulated our nanomagnet reservoir computer (NMRC) design on benchmark tasks, demonstrating NMRCs high memory content and expressibility. In support of the feasibility of this method, we fabricated a frustrated nanomagnet reservoir layer. Using th
We demonstrate reservoir computing with a physical system using a single autonomous Boolean logic element with time-delay feedback. The system generates a chaotic transient with a window of consistency lasting between 30 and 300 ns, which we show is
Reservoir computing is a computational framework suited for temporal/sequential data processing. It is derived from several recurrent neural network models, including echo state networks and liquid state machines. A reservoir computing system consist