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Related Experiment Video

Updated: Jun 26, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Controlling chaos using edge computing hardware.

Robert M Kent1, Wendson A S Barbosa1, Daniel J Gauthier2,3

  • 1The Ohio State University, Department of Physics, 191 West Woodruff Ave., Columbus, OH, 43210, USA.

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|May 8, 2024
PubMed
Summary
This summary is machine-generated.

Next-generation reservoir computing creates accurate, low-power digital twins for controlling chaotic systems on embedded hardware. This machine learning approach enables edge computing applications without cloud connections.

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Area of Science:

  • Artificial Intelligence
  • Control Systems Engineering
  • Embedded Systems

Background:

  • Digital twins, data-driven models predicting system behavior, are crucial for applications like autonomous systems control.
  • Minimizing the size, weight, and power (SWaP) of digital twins and controllers is essential for deployment on resource-constrained embedded hardware.
  • Edge computing, performing computations locally without cloud reliance, necessitates efficient algorithms for embedded devices.

Purpose of the Study:

  • To demonstrate a novel nonlinear controller based on next-generation reservoir computing for controlling chaotic systems.
  • To evaluate the feasibility of deploying this controller on embedded computing hardware, specifically a field-programmable gate array (FPGA).
  • To assess the power efficiency of the reservoir computing model for edge computing applications.

Main Methods:

  • Developed a nonlinear controller utilizing next-generation reservoir computing principles.
  • Implemented and evaluated the controller's performance on a chaotic system, targeting arbitrary time-dependent states.
  • Measured the computational resource requirements (size, weight) and energy consumption (power) of the model on an FPGA.

Main Results:

  • The reservoir computing-based controller accurately controlled a chaotic system to a desired time-dependent state.
  • The developed model is sufficiently small for evaluation on typical embedded devices like FPGAs.
  • The model demonstrated remarkable energy efficiency, requiring only 25.0 nJ per evaluation, outperforming other algorithms.

Conclusions:

  • Next-generation reservoir computing offers a viable solution for creating accurate and computationally efficient digital twins for complex control tasks.
  • The developed controller can be deployed on edge devices, enabling autonomous system control without cloud connectivity.
  • This research marks a significant advancement in deploying efficient machine learning algorithms to the edge, paving the way for powerful embedded AI.