Multi-State Probabilistic Computing Using Floating-Body MOSFETs Based on the Potts Model for Solving Complex Combinatorial Optimization Problems.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41700537.
- Also identified by DOI 10.1002/adma.202516797 and PMC identifier 12994325.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Probabilistic computing has gained attention for solving combinatorial optimization problems (COPs), mainly using the Ising model, which may not be suitable for complex COPs. Instead, this work proposes a multi-state probabilistic computing system based on the Potts model using stochastic threshold switching floating-body metal-oxide-semiconductor field-effect transistors (FB-MOSFETs) as the multi-state probabilistic bits (p-bits) to solve challenging COPs. The system employs drain voltage sharing and a one-hot sampling method to achieve controllable probabilistic behavior and scalable annealing. Experimental validations on spin glass and max-4-cut problems demonstrate that the system efficiently samples a tunable Boltzmann distribution while converging faster than traditional methods. Comparative analyses further highlight superior energy efficiency and decreased time-to-solution, underscoring the potential of multi-state probabilistic computing for large-scale, complex COPs using only MOSFET devices.