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Magnetic-Tunnel-Junction-Based Probabilistic Processor for Efficient Embodied Evolution.

Youwei Bao1, Shuhan Yang1, Edward Humianto1

  • 1Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore.

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Summary
This summary is machine-generated.

This study introduces a 256-magnetic-tunnel-junction (MTJ) probabilistic processor for efficient embodied evolution. This low-power hardware enables evolutionary robotics by generating stochasticity for AI adaptation and selection.

Keywords:
embodied artificial intelligencemagnetic tunnel junctionsprobabilistic computing hardwarereconfigurable random number generator

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Materials Science

Background:

  • Embodied AI uses evolutionary search for adaptation in complex environments.
  • Current hardware pseudorandom number generators are power-intensive.
  • Magnetic-tunnel-junctions (MTJs) offer potential for efficient stochastic computing.

Purpose of the Study:

  • To present an efficient physical platform for embodied evolution using MTJ-based probabilistic hardware.
  • To demonstrate the effectiveness of MTJ-based stochasticity for AI adaptation and on-hardware selection.

Main Methods:

  • Developed a 256-MTJ probabilistic processor.
  • Implemented an Ising-based probabilistic sampler to create reconfigurable Gaussian probabilistic bits.
  • Scaled the architecture for parallel generation of evolutionary mutations.
  • Integrated on-hardware selection, including a diversity-aware scheme.

Main Results:

  • Achieved evolved agent morphologies comparable to software-based ideal generators in robotic locomotion.
  • Demonstrated the capability of MTJ-based hardware for evolutionary mutation and selection.
  • Showcased low-power stochastic computation using MTJ networks.

Conclusions:

  • MTJ-based probabilistic hardware is a viable, low-power substrate for embodied AI.
  • This approach enables efficient evolutionary robotics and on-hardware adaptation.
  • Future work can explore larger-scale MTJ-based systems for complex AI tasks.