With massive numbers of data points requiring real-time processing every day, limitations of cloud computing, including leaks, communications delays, slow speeds, and higher power consumption need to be addressed. Edge computing, which distributes computations to reduce load and speed processing, presents an alternative.
However, for effective edge computing, efficient and computationally cost-effective technology is needed. One promising 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 nonlinearly to them.
In particular, physical reservoirs, which use the dynamics of physical systems, are both 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 limits real-time processing and requires adjustments to improve performance.
Recently, researchers at the Tokyo University of Science (TUS) developed an optical device with features that support physical reservoir computing and allow real-time signal processing across a broad range of timescales within a single device.
Applied physics professor Kentaro Kinoshita and his team created a device using Sn-doped In2O3 and Nb-doped SrTiO3 (denoted as ITO/Nb:STO), which responds to both electrical and optical signals.
“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 realize a physical reservoir in which the relaxation time of photo-induced current can be arbitrarily controlled by voltage,” Kinoshita said.
The team tested the electrical features of the device to confirm that it functions as a memristor and explored how it responds to ultraviolet light 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.
Furthermore, the team tested the effectiveness of ITO/Nb:STO as a physical reservoir by using it for classifying handwritten digit images in the MNIST (Modified National Institute of Standards and Technology) data set. The device achieved a classification accuracy of up to 90.2%. Additionally, to understand the role of the physical reservoir, the team ran experiments without it, which resulted in a relatively lower classification accuracy of 85.1%. These findings show that the ITO/Nb:STO junction device improves classification accuracy while keeping computational costs lower, proving its value as a physical reservoir.
Kinoshita said that the device will enable a single device to process real-time signals with various timescales generated in the real world, in real time.