利用边缘计算硬件控制混沌
Controlling Chaos Using Edge Computing Hardware
AI总结：
本研究提出基于下一代储层计算的非线性控制器，可将混沌系统控制至任意时变状态，且模型小、功耗极低，能在嵌入式FPGA硬件上运行，为高效机器学习算法部署到边缘计算迈出第一步。
摘要：
机器学习为构建系统的数字孪生（即用于预测系统行为的数字模型）提供了一种数据驱动的方法。拥有准确的数字孪生可推动诸多应用，例如自主系统的控制。通常，数字孪生或相关控制器的尺寸、重量与功耗必须降至最低，理想情况下可在无需云计算连接的嵌入式计算硬件上实现。本研究表明，基于下一代储层计算（reservoir computing）的非线性控制器可解决一项高难度控制问题：将混沌系统控制至任意随时间变化的状态。该模型精度高，且体积足够小，可在嵌入式设备中常见的现场可编程门阵列（FPGA）上运行。此外，该模型每次评估仅需25.0±7.0纳焦，远低于其他算法，甚至在未进行系统功耗优化的情况下亦是如此。本研究是将高效机器学习算法部署到计算“边缘”的第一步。
英文摘要：
Machine learning provides a data-driven approach for creating a digital twin of a system - a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedded computing hardware that can operate without a cloud-computing connection. Here, we show that a nonlinear controller based on next-generation reservoir computing can tackle a difficult control problem: controlling a chaotic system to an arbitrary time-dependent state. The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array typically found in embedded devices. Furthermore, the model only requires 25.0 $\pm$ 7.0 nJ per evaluation, well below other algorithms, even without systematic power optimization. Our work represents the first step in deploying efficient machine learning algorithms to the computing "edge."
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
