Physics-inspired Ising Computing with Ring Oscillator Activated p-bits
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Abstract (arXiv)
The nearing end of Moore's Law has been driving the development of domain-specific hardware tailored to solve a special set of problems. Along these lines, probabilistic computing with inherently stochastic building blocks (p-bits) have shown significant promise, particularly in the context of hard optimization and statistical sampling problems. p-bits have been proposed and demonstrated in different hardware substrates ranging from small-scale stochastic magnetic tunnel junctions (sMTJs) in asynchronous architectures to large-scale CMOS in synchronous architectures. Here, we design and implement a truly asynchronous and medium-scale p-computer (with ≈ 800 p-bits) that closely emulates the asynchronous dynamics of sMTJs in Field Programmable Gate Arrays (FPGAs). Using hard instances of the planted Ising glass problem on the Chimera lattice, we evaluate the performance of the asynchronous architecture against an ideal, synchronous design that performs parallelized (chromatic) exact Gibbs sampling. We find that despite the lack of any careful synchronization, the asynchronous design achieves parallelism with comparable algorithmic scaling in the ideal, carefully tuned and parallelized synchronous design. Our results highlight the promise of massively scaled p-computers with millions of free-running p-bits made out of nanoscale building blocks such as stochastic magnetic tunnel junctions.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 May 2022.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 2.
4 papers.
- 2023Sub-universal variational circuits for combinatorial optimization problemscited by 1
- 2024Solving Boltzmann Optimization Problems with Deep Learningcited by 0
- 2026GPU-accelerated simulated annealing based on p-bits with real-world device-variability modelingcited by 0
- 2026Enhanced Convergence in p-bit Based Simulated Annealing with Partial Deactivation for Large-Scale Combinatorial Optimization Problemscited by 0
- 2026Enhanced Convergence in p-bit Based Simulated Annealing with Partial Deactivation for Large-Scale Combinatorial Optimization Problems22 Jan 2026
- 2026GPU-accelerated simulated annealing based on p-bits with real-world device-variability modeling20 Jan 2026
- 2024Solving Boltzmann Optimization Problems with Deep Learning30 Jan 2024
- 2023Sub-universal variational circuits for combinatorial optimization problems29 Aug 2023