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Harnessing intuitive local evolution rules for physical learning.

Roie Ezraty1, Menachem Stern2, Shmuel M Rubinstein1

  • 1Hebrew University of Jerusalem, Racah Institute of Physics, Jerusalem 9190401, Israel.

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Summary
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We developed a novel training scheme for physical systems, BEASTAL, that minimizes power consumption for machine learning tasks. This Boundary-Enabled Adaptive State Tuning System (BEASTS) approach enables physical systems to learn using local rules.

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Area of Science:

  • Physics
  • Computer Science
  • Machine Learning

Background:

  • Machine learning is computationally intensive and power-consuming.
  • Interest is growing in physical implementations of learning tasks to reduce energy use.

Purpose of the Study:

  • Introduce a training scheme for physical systems that minimizes power dissipation.
  • Develop a physical analog of the Adaline algorithm for autonomous learning.

Main Methods:

  • Utilize Boundary-Enabled Adaptive State Tuning Systems (BEASTS) that exploit local physical rules.
  • Implement the BEASTAL (BEAST-Adaline) training scheme, controlling only boundary parameters (inputs and outputs).
  • Demonstrate autonomous learning in silico for regression and classification tasks.

Main Results:

  • BEASTAL systems learn by exploiting local physical rules with minimal power dissipation.
  • Autonomous learning was successfully demonstrated for regression and classification tasks.
  • The approach avoids large-scale memory and complex internal architectures.

Conclusions:

  • BEASTAL advances physical learning schemes by using intuitive, local evolution rules.
  • This method is applicable to any physical system with a linear input-output map.
  • Best performance is achieved when the local evolution rule is nonlinear.