ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers
<|reference_start|>ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers: We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using an annealing process that is governed by the physics of quantum mechanical tunneling using Fowler-Nordheim (FN). The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing (SA) dynamics onto a network of integrate-and-fire (IF) neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer which 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 various benchmark MAX-CUT combinatorial optimization problems. Across multiple runs, NeuroSA consistently generates solutions that approach the state-of-the-art level with high accuracy (greater than 99%), and 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.<|reference_end|>
@article{chen2024on-off,
    title={ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers},
    author={Zihao Chen, Zhili Xiao, Mahmoud Akl, Johannes Leugring, Omowuyi
  Olajide, Adil Malik, Nik Dennler, Chad Harper, Subhankar Bose, Hector A.
  Gonzalez, Jason Eshraghian, Riccardo Pignari, Gianvito Urgese, Andreas G.
  Andreou, Sadasivan Shankar, Christian Mayr, Gert Cauwenberghs, Shantanu
  Chakrabartty},
    journal={arXiv preprint arXiv:2406.05224},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05224},
    primaryClass={cs.NE}
}