# ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers

arXiv: https://arxiv.org/abs/2406.05224 · HTML: https://mlsanity.com/paper/2406.05224/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2024.

## Built on by (most cited first, 6 of 6)

- [Full-Precision and Ternarised Neural Networks with Tunnel-Diode Activation Functions: Computing and Physics Perspectives](https://mlsanity.com/paper/2503.04978/) (2025)
- [Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems](https://mlsanity.com/paper/2412.05126/) (2024)
- [Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications](https://mlsanity.com/paper/2503.07681/) (2025)
- [Efficient Optimization Accelerator Framework for Multistate Ising Problems](https://mlsanity.com/paper/2505.20250/) (2025)
- [Local-Minima-Preserving Continuous Relaxation of Ising Problems](https://mlsanity.com/paper/2606.30333/) (2026)
- [The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing](https://mlsanity.com/paper/2607.24396/) (2026)

## Abstract (arXiv)

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer and the resulting spiking dynamics replicates the optimal escape mechanism and convergence of SA, particularly at low-temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and Max Independent Set. Across multiple runs, NeuroSA consistently generates distribution of solutions that are concentrated around the state-of-the-art results (within 99%) or surpass the current state-of-the-art solutions for Max Independent Set benchmarks. Furthermore, NeuroSA is able to achieve these superior distributions without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform.