Advancement in Time-Series Information: Development of 3D-Integrated Artificial Neural Network for Learning and Forecasting

Professor Wang Gun-uk’s group at the KU-KIST Graduate School of Converging Science and Technology/Department of Integrative Energy Engineering and Professor J. Joshua Yang’s group at the University of Southern California (USC), US, implemented a vertically multilayered 3D physical reservoir array, thus presenting a novel hardware platform capable of learning and forecasting time-series information.

The research results were published online on March 6 in Nature Communications (IF=16.6), a renowned journal in multidisciplinary research.
Recently, time-series data, which are continuous observations collected during a specific time frame, have gradually gained more importance as a dynamic source of information in applications such as biometric analysis, weather forecasting, stock chart estimation, and hydrological inflow forecasting.

In particular, wide reservoir computing, which has been theoretically proposed, is an advanced reservoir structure that allows for processing time-series data that is difficult to learn and forecast, using multiple reservoir layers but, to date, the implementation of wide reservoir computing has been a very difficult task due not only to the absence of a high-performance physical reservoir but also to the complexity and unstable operation associated with vertically stacking multiple physical reservoir layers.

In particular, wide reservoir computing, which has been theoretically proposed, is an advanced reservoir structure that allows for processing time-series data that is difficult to learn and forecast, using multiple reservoir layers but, to date, the implementation of wide reservoir computing has been a very difficult task due not only to the absence of a high-performance physical reservoir but also to the complexity and unstable operation associated with vertically stacking multiple physical reservoir layers.

In this study, the researchers designed and manufactured a three-dimensional multilayered physical system using a next-generation electronic device called a memristor and implemented a wide reservoir computing system composed of multiple reservoir layers in the hardware (Figure 1), producing a hardware platform that can efficiently process multiple streams of time-series information with dynamicity, which is more complicated and difficult to forecast.

In particular, because the newly developed system allows for the effective understanding and learning of the subtle data features present in complex time-series data, it is highly efficient in processing complex time-series data compared to the 2D approaches that were extensively studied in the past (Figure 2). Based on this, their results are expected to play an important role in providing groundbreaking directions for physical reservoir computing that allows for the efficient processing of multiple streams of dynamic time-series information.

This study was supported by the National Research Foundation of Korea and the Korea Institute of Science and Technology (KIST) Institutional Program.