Pushing the frontiers of time-series information processing in ever-growing edge devices with stringent resources has been impeded by the system's ability to process information and learn locally on the device. Local processing and learning typically demand intensive computations and massive storage as the process involves retrieving information and tuning hundreds of parameters back in time. In this work, we developed a memristor-based echo state network accelerator that features efficient temporal data processing and in-situ online learning. The proposed design is benchmarked using various datasets involving real-world tasks, such as forecasting the load energy consumption and weather conditions. The experimental results illustrate that the hardware model experiences a marginal degradation (~4.8%) in performance as compared to the software model. This is mainly attributed to the limited precision and dynamic range of network parameters when emulated using memristor devices. The proposed system is evaluated for lifespan, robustness, and energy-delay product. It is observed that the system demonstrates a reasonable robustness for device failure below 10%, which may occur due to stuck-at faults. Furthermore, 246X reduction in energy consumption is achieved when compared to a custom CMOS digital design implemented at the same technology node.

翻译：在不断增长的边缘设备中，由于资源受限，时间序列信息处理的前沿发展一直受到系统在设备本地处理信息和学习能力的制约。本地处理和学习通常需要密集的计算和海量的存储，因为该过程涉及回溯检索信息并调整数百个参数。在本研究中，我们开发了一种基于忆阻器的回声状态网络加速器，该加速器具有高效时序数据处理和原位在线学习的特点。所提出的设计使用涉及实际任务的各种数据集进行基准测试，例如预测负荷能耗和天气条件。实验结果表明，与软件模型相比，硬件模型的性能仅出现轻微下降（约4.8%）。这主要归因于使用忆阻器器件仿真时网络参数的精度和动态范围有限。对所提系统的寿命、鲁棒性和能量延迟积进行了评估。观察到在故障率低于10%（可能由固定型故障引起）时，系统表现出合理的鲁棒性。此外，与相同技术节点下实现的定制CMOS数字设计相比，能耗降低了246倍。