Journal Article

Reservoir computing using dynamic memristors for temporal information processing

Lu, Wei D., Du, Chao, Ma, Wen, Zidan, Mohammed A., Lee, Seung Hwan, Cai, Fuxi

2017

Overview

Reservoir computing systems utilize dynamic reservoirs having short-term memory to project features from the temporal inputs into a high-dimensional feature space. A readout function layer can then effectively analyze the projected features for tasks, such as classification and time-series analysis. The system can efficiently compute complex and temporal data with low-training cost, since only the readout function needs to be trained. Here we experimentally implement a reservoir computing system using a dynamic memristor array. We show that the internal ionic dynamic processes of memristors allow the memristor-based reservoir to directly process information in the temporal domain, and demonstrate that even a small hardware system with only 88 memristors can already be used for tasks, such as handwritten digit recognition. The system is also used to experimentally solve a second-order nonlinear task, and can successfully predict the expected output without knowing the form of the original dynamic transfer function. Reservoir computing facilitates the projection of temporal input signals onto a high-dimensional feature space via a dynamic system, known as the reservoir. Du et al. realise this concept using metal-oxide-based memristors with short-term memory to perform digit recognition tasks and solve non-linear problems.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/166/987 / 639/925/927/1007 / Computer memory / Computing time / Data processing / Handwriting / Handwriting recognition / Humanities and Social Sciences / Information processing / Information systems / Memristors / multidisciplinary / Neural networks / Neurons / Science / Science (multidisciplinary) / Short term memory / Time series / Transfer functions