ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers

• URL: http://arxiv.org/abs/2406.05224v2
• Date: Sat, 01 Mar 2025 22:41:01 GMT
• Title: ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers
• Authors: Zihao Chen, Zhili Xiao, Mahmoud Akl, Johannes Leugring, Omowuyi Olajide, Adil Malik, Nik Dennler, Chad Harper, Subhankar Bose, Hector A. Gonzalez, Mohamed Samaali, Gengting Liu, Jason Eshraghian, Riccardo Pignari, Gianvito Urgese, Andreas G. Andreou, Sadasivan Shankar, Christian Mayr, Gert Cauwenberghs, Shantanu Chakrabartty,
• Abstract summary: We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure convergence to the ground state of an Ising problem.<n>Across multiple runs, NeuroSA consistently generates 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.<n>For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform.
• Score: 4.429465736433621
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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