Physical reservoir computing (PRC), a brain-inspired computing method known for its efficient information processing and low training requirements, has attracted significant attention. The key factor lies in the number of computational nodes within the reservoir for its computational capability. Here, we explore co-multiplexing reservoirs that leverage both temporal and spatial strategies. Temporal multiplexing virtually expands the node count through the use of masking techniques, while spatial multiplexing utilizes multiple physical locations (e.g., Hall bars) to achieve an increase in the number of real nodes. Our experiment employs a strain-mediated reservoir based on multiferroic heterostructures. By applying a single voltage across the PMN-PT substrate (acting as global input) and measuring the output Hall voltages from four Hall bars (real nodes), we achieve significant efficiency gains. This co-multiplexing approach results in a reduction in the normalized root mean square error from 0.5 to 0.23 for a 20-step prediction task of a Mackey–Glass chaotic time series. Furthermore, the single input and four independent outputs lead to a fourfold reduction in energy consumption compared to the strain-mediated PRC with temporal multiplexing solely. This research paves the way for future energy saving PRC implementations utilizing co-multiplexing, promoting a resource-efficient paradigm in reservoir computing.

中文翻译：

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多铁异质结构中具有时间和空间共复用的应变介导储层计算


物理储层计算 （PRC） 是一种以高效信息处理和低训练要求而闻名的类脑计算方法，引起了广泛关注。关键因素在于储层内计算节点的数量，以实现其计算能力。在这里，我们探索了利用时间和空间策略的共多路复用储层。时间多路复用通过使用掩码技术虚拟地扩展了节点数量，而空间多路复用则利用多个物理位置（例如霍尔条）来增加实际节点的数量。我们的实验采用了基于多铁异质结构的应变介导的储层。通过在 PMN-PT 衬底上施加单个电压（充当全局输入）并测量来自四个霍尔棒（实节点）的输出霍尔电压，我们实现了显著的效率提升。这种协多路复用方法可将 Mackey-Glass 混沌时间序列的 20 步预测任务的归一化均方根误差从 0.5 减少到 0.23。此外，与仅使用时间多路复用的应变介导的 PRC 相比，单个输入和四个独立输出使能耗降低了四倍。这项研究为未来利用共复用的节能 PRC 实施铺平了道路，促进了储层计算中的资源高效范式。