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A parallel magnetic tunnel junction-based probabilistic Ising processor for efficient quadratic optimization.

Shuhan Yang1, Youwei Bao1, Edward Humianto1

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

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Summary
This summary is machine-generated.

This study introduces a novel spintronics-based probabilistic processor for complex optimization tasks. The system offers significant speedups and energy savings compared to conventional and quantum computing methods.

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Area of Science:

  • Spintronics
  • Combinatorial Optimization
  • Probabilistic Computing

Background:

  • Conventional computing struggles with computationally intensive optimization problems, being slow and energy-hungry.
  • Quantum computing shows promise for optimization but is still in early development.
  • Probabilistic computing presents a viable near-term solution for accelerating optimization using stochastic methods.

Purpose of the Study:

  • To experimentally demonstrate a scalable spintronics-based probabilistic processor for solving quadratic assignment problems.
  • To evaluate the system's performance against conventional and quantum computing approaches.

Main Methods:

  • Utilized a scalable spin-transfer-torque-magnetic-tunnel-junction based probabilistic processor.
  • Integrated 144 spintronics tunable random number generators in a massively parallel architecture.
  • Co-designed a parallel trial annealing scheme for optimization.

Main Results:

  • Achieved a Monte Carlo sampling throughput of 14.4 million flips per second.
  • Demonstrated a 123x speedup and 98.3% energy savings over conventional Gibbs sampling.
  • Showed a 3.2x speedup and 58.3% energy savings compared to CPU implementation.
  • Outperformed GPU and D-Wave quantum annealers in solution quality, speed, and energy efficiency.

Conclusions:

  • The demonstrated spintronics probabilistic processor offers a practical and efficient solution for complex optimization problems.
  • This technology provides significant advantages in speed and energy efficiency over existing computing paradigms.
  • The system shows potential for advancing the field of combinatorial optimization.