Information Processing by Nonlinear Phase Dynamics in Locally Connected Arrays

TL;DR

This paper explores a novel approach to information processing using nonlinear phase dynamics in locally connected arrays, aiming to overcome interconnect bottlenecks through scalable, nano-scale architectures that leverage phase interactions.

Contribution

It introduces a scheme where logic states are defined by electrical phase, and demonstrates potential via simulations of neuron-like elements in nano-architectures.

Findings

Simulation results show effective phase-based logic processing.

Potential for ultra-small, scalable nano-architectures.

Highlights advantages of parallelism in phase dynamics.

Abstract

Research toward powerful information processing systems that circumvent the interconnect bottleneck by exploiting the nonlinear evolution of multiple phase dynamics in locally connected arrays is discussed. We focus on a scheme in which logic states are defined by the electrical phase of a dynamic process and information processing is realized through interactions between the elements in the array. Simulation results are given for networks comprised of neuron-like integrate-and-fire elements, which could potentially be implemented by ultra-small tunnel junctions, molecules and other types of nanoscale elements. This approach could lead to powerful information processing systems due to massive parallelism in simple, highly scalable nano-architectures. The rational for this approach, its advantages, simulation results, critical issues, and future research directions are discussed.