IT Matters
Traditional cloud computing faces various challenges when processing large amounts of data in real time. “Edge” computing is a promising alternative and can benefit from devices known as physical reservoirs. Researchers have now developed a novel memristor device for this purpose. It responds to electrical and optical signals and overcomes real-time processing limitations. When tested, it achieved up to 90.2% accuracy in digit identification, demonstrating its potential for applications in artificial intelligence systems and beyond.
Every day, a significant amount of data related to weather, traffic, and social media undergo real-time processing.
In traditional cloud computing, this processing occurs on the cloud, raising concerns about issues such as leaks, communication delays, slow speeds, and higher power consumption.
Against this backdrop, “edge computing” presents a promising alternative solution.
Located near users, it aims to distribute computations, thereby reducing the load and speeding up data processing.
Specifically, edge AI, which involves AI processing at the edge, is expected to find applications in, for example, self-driving cars and machine anomaly prediction in factories.
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 for best learning performance.
Recently, Professor Kentaro Kinoshita, a member of the Faculty of Advanced Engineering and the Department of Applied Physics at the Tokyo University of Science (TUS), and Mr. Yutaro Yamazaki from the Graduate School of Science and the same department at 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.
