Reservoir Computing Approach to Robust Computation using Unreliable Nanoscale Networks

TL;DR

This paper explores using reservoir computing with nanoscale networks to perform robust computation despite device variability, noise, and defects inherent in nanotechnology-based substrates.

Contribution

It introduces a reservoir computing framework tailored for unreliable nanoscale networks, demonstrating intrinsic robustness through a theoretical model.

Findings

Reservoir computing can handle structural noise in nanoscale devices.

Both regular and irregular reservoirs are inherently robust to fabrication imperfections.

The approach enables computation without external control or redundancy.

Abstract

As we approach the physical limits of CMOS technology, advances in materials science and nanotechnology are making available a variety of unconventional computing substrates that can potentially replace top-down-designed silicon-based computing devices. Inherent stochasticity in the fabrication process and nanometer scale of these substrates inevitably lead to design variations, defects, faults, and noise in the resulting devices. A key challenge is how to harness such devices to perform robust computation. We propose reservoir computing as a solution. In reservoir computing, computation takes place by translating the dynamics of an excited medium, called a reservoir, into a desired output. This approach eliminates the need for external control and redundancy, and the programming is done using a closed-form regression problem on the output, which also allows concurrent programming using a single device. Using a theoretical model, we show that both regular and irregular reservoirs are intrinsically robust to structural noise as they perform computation.