Metrics for spin-based computing

Area of Science:

• Materials Science
• Computer Engineering
• Quantum Computing

Background:

• Spintronic devices utilize electron spin dynamics for non-volatile, nonlinear, and fast operations.
• These devices can integrate with photonic and phononic systems, offering versatile functionalities.

Purpose of the Study:

• To review key advances in integrating magnetic and spintronic elements into computational architectures.
• To discuss metrics for evaluating spin-based computing performance and their physical property correlations.
• To identify challenges and future opportunities in spin-based computing.

Main Methods:

• Review of fundamental spintronic components (e.g., radio-frequency neurons/synapses, probabilistic-bits).
• Exploration of broader computational frameworks (e.g., reservoir computing, magnetic Ising machines).
• Analysis of hardware-specific and task-dependent performance metrics.

Main Results:

• Integration of magnetic and spintronic elements into diverse computational architectures.
• Development of metrics to assess computing performance based on physical properties.
• Identification of specific components and frameworks driving progress.

Conclusions:

• Spin-based computing offers a promising path for energy-efficient and high-performance data processing.
• Further research is needed to overcome challenges and realize the full potential of next-generation spintronic technologies.