Optical device designed to facilitate edge computing

Every day, a significant amount of data related to weather, traffic and social media undergoes real-time processing. In traditional cloud computing, this processing occurs on the cloud, raising concerns about leaks, communication delays, slow speeds and higher power consumption. Edge computing presents a promising alternative solution; located near users, it aims to distribute computations, thereby reducing the load and speeding up data processing.

However, for effective edge computing, efficient and computationally cost-effective technology is needed. One option is reservoir computing, a computational method designed for processing signals that are recorded over time. It can transform these signals into complex patterns using reservoirs that respond non-linearly to them. In particular, physical reservoirs, which use the dynamics of physical systems, are computationally cost-effective and efficient. However, their ability to process signals in real time is limited by the natural relaxation time of the physical system; this can limit real-time processing and requires adjustments for best learning performance.

Researchers from the Tokyo University of Science (TUS) have developed an optical device with features that support physical reservoir computing and allow real-time signal processing across a range of timescales within a single device. The research findings were published in Advanced Science. Professor Kentaro Kinoshita from TUS said the devices developed in this research will enable a single device to process time-series signals with various timescales generated in real time. “In particular, we hope to realise an AI device to utilise in the edge domain,” Kinoshita said.

The researchers created the device using Sn-doped In2O3 and Nb-doped SrTiO3 (denoted as ITO/Nb:STO), which responds to electrical and optical signals. They tested the electrical features of the device to confirm that it functions as a memristor (a memory device that can change its electrical resistance). The researchers also explored the influence of ultraviolet light on ITO/Nb:STO by varying the voltage and observing changes in the current. The results suggested that this device can modify the relaxation time of the photo-induced current according to the voltage, making it a potential candidate for a physical reservoir.

In order to understand the role of the physical reservoir, the team ran experiments without it, which resulted in a low classification accuracy of 85.1%. These findings show that the ITO/Nb:STO junction device improves classification accuracy while keeping computational costs low, proving its value as a physical reservoir.

“In the past, our research group has focused on research and development of materials applicable to physical reservoir computing. Accordingly, we fabricated these devices with the aim to realise a physical reservoir in which the relaxation time of photo-induced current can be arbitrarily controlled by voltage,” Kinoshita said.

This study presents a novel memristor device capable of adjusting its response timescale through voltage variation, exhibiting enhanced learning capabilities, which makes it suitable for applications such as an AI device for edge computing. This, in turn, could pave the way for single devices that can handle signals of varied durations found in real-world environments.

Image credit: iStock.com/Funtap