Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA.
Nat Commun. 2017 Dec 19;8(1):2204. doi: 10.1038/s41467-017-02337-y.
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.
储层计算系统利用具有短期记忆的动态储层，将时间输入的特征投影到高维特征空间中。然后，读取功能层可以有效地分析所投影的特征，以完成分类和时间序列分析等任务。由于仅需对读取功能层进行训练，因此该系统可以高效地计算复杂的时间数据，并且训练成本低。在这里，我们使用动态忆阻器阵列实验实现了储层计算系统。我们表明，忆阻器的内部离子动态过程允许基于忆阻器的储层直接在时域中处理信息，并证明即使是只有 88 个忆阻器的小型硬件系统也可用于执行手写数字识别等任务。该系统还用于实验解决二阶非线性任务，可以在不知道原始动态传递函数形式的情况下成功预测预期输出。
