Chaotic Amplitude Control for Neuromorphic Ising Machine in Silico was submitted to arXiv
- Combinatorial Optimization
Title: Chaotic Amplitude Control for Neuromorphic Ising Machine in Silico [arXiv, PDF]
Authors: Timothee Leleu, Farad Khoyratee, Timothee Levi, Ryan Hamerly, Takashi Kohno, Kazuyuki Aihara
Submitted on 9 September 2020.
Abstract: Ising machines are special-purpose hardware designed to reduce time, resources, and energy consumption needed for finding low energy states of the Ising Hamiltonian. In recent years, most of the physical implementations of such machines have been based on a similar concept that is closely related to annealing such as in simulated, mean-field, chaotic, and quantum annealing. We argue that Ising machines benefit from implementing a chaotic amplitude control of mean field dynamics that does not rely solely on annealing for escaping local minima trap. We show that this scheme outperforms recent state-of-the-art solvers for some reference benchmarks quantitatively and qualitatively in terms of time to solution, energy efficiency, and scaling of its variance with problem size when it is implemented in hardware such as in a field programmable gate array that is organized according to a neuromorphic design where memory is distributed with processing.
