Artificial Intelligence Goes Physical

Abstract

Exploiting the intrinsic nonlinearity in physical reservoirs, e.g., dopant‐atom networks, provides a new approach toward highly efficient computing such as feature projection and classification. In a recent study by Chen et al., the computational capability of dopant‐atom network was investigated and found to diminish as the signal‐to‐noise ratio (SNR) increased, indicating the existence of an optimal bias condition. Although high SNR is often pursued in signal processing, it shows that embracing noise in non‐conventional computing systems may lead to a leap in computing capacity. This work showcased that material or device physics in different domains offer valuable substrates for complex computing functions and high energy efficiency.

Keywords

• artificial intelligence
• nonlinearity
• noise
• reservoir computing