Corpus ID: 221556362

Chaotic Amplitude Control for Neuromorphic Ising Machine in Silico

  title={Chaotic Amplitude Control for Neuromorphic Ising Machine in Silico},
  author={Timoth{\'e}e G. Leleu and Farad Khoyratee and Timoth{\'e}e Levi and Ryan Hamerly and Takashi Kohno and Kazuyuki Aihara},
  journal={arXiv: Computational Physics},
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… Expand
High-performance combinatorial optimization based on classical mechanics
This work proposes an algorithm based on classical mechanics, which is obtained by modifying a previously proposed algorithm called simulated bifurcation, which allows us to achieve not only high speed by parallel computing but also high solution accuracy for problems with up to one million binary variables. Expand
Ising Machines' Dynamics and Regularization for Near-Optimal Large and Massive MIMO Detection
This work proposes a novel regularized Ising formulation for MIMO detection that mitigates the error floor and further evolves it into an algorithm that achieves near-optimal MIMo detection. Expand


Emulating the coherent Ising machine with a mean-field algorithm
The coherent Ising machine is an optical processor that uses coherent laser pulses, but does not employ coherent quantum dynamics in a computational role. Core to its operation is the iteratedExpand
A Recurrent Ising Machine in a Photonic Integrated Circuit
This work experimentally demonstrates a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS) capable of converging to the ground state of various 4-spin graphs with high probability and paves a way for orders- of-magnitude speedups in exploring the solution space of combinatorially hard problems. Expand
Harnessing Intrinsic Noise in Memristor Hopfield Neural Networks for Combinatorial Optimization
A memristor-Hopfield Neural Network with massively parallel operations performed in a dense crossbar array with substantially improved performance and scalability compared to current quantum annealing approaches, while operating at room temperature and taking advantage of existing CMOS technology augmented with emerging analog non-volatile memristors. Expand
Optimization of the Sherrington-Kirkpatrick Hamiltonian
  • A. Montanari
  • Computer Science, Mathematics
  • 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS)
  • 2019
This work gives an algorithm that, for any ε > 0, outputs a feasible solution whose value is at least (1 – ε) of the optimum, with probability converging to one as the dimension n of the matrix diverges. Expand
In Advances in Neural Information Processing Systems
Bill Baird { Publications References 1] B. Baird. Bifurcation analysis of oscillating neural network model of pattern recognition in the rabbit olfactory bulb. In D. 3] B. Baird. Bifurcation analysisExpand
Ising Model Optimization Problems on a FPGA Accelerated Restricted Boltzmann Machine
This work demonstrates usage of the Restricted Boltzmann Machine (RBM) as a stochastic neural network capable of solving NP-Hard Combinatorial Optimization problems efficiently and shows that by mapping the RBM onto a reconfigurable Field Programmable Gate Array (FPGA), it is shown that by using commodity hardware running at room temperature for acceleration, the R BM has greater potential for immediate and scalable use. Expand
IEEE Transactions on Pattern Analysis and Machine Intelligence
  • K. Fu
  • Computer Science
  • 2004
FPGA-CAC finds solutions of better quality than previously known from (52)
  • 2000
  • G. Berloff, Scientific reports 8,
  • 1779