GHMC simulated annealing algorithm-based FPGA parallel Isin model optimization method and system

Parallel Ising model optimization on FPGA using the GHMC simulated annealing algorithm solves the problems of low convergence accuracy and low solution quality of traditional Monte Carlo methods, and achieves efficient global optimal solution finding, which is suitable for complex optimization scenarios such as power system dispatching.

CN121031808APending Publication Date: 2025-11-28YISI GYROMAGNETIC (JIAXING) ELECTRONICS CO LTD
0 Cites 3 Cited by

Patent Information

Application Number
CN202511131357.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-13
Publication Date
2025-11-28

AI Technical Summary

Technical Problem

Existing FPGA-based Ising model solvers employ the traditional Monte Carlo method, which suffers from low convergence accuracy and low solution quality, making it difficult to efficiently find the global optimal solution in complex energy barrier scenarios.

Method used

An FPGA-based parallel Ising model optimization method based on the GHMC simulated annealing algorithm is adopted. The problem is mapped to the Hamiltonian parameters of the Ising model by the host computer. The solution is parallelized on the FPGA using a hybrid GHMC sampler and a temperature scheduling module. By combining Leapfrog integration and Metropolis acceptance mechanism, the system temperature is gradually reduced to drive the Ising model to converge to the global optimum.

Benefits of technology

It significantly improves the convergence speed and solution quality of the Ising model, achieves efficient global optimal solution finding, and combines advanced algorithm with high hardware efficiency, making it suitable for high real-time demand scenarios such as power systems.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention provides an FPGA parallel Isin model optimization method and system based on a GHMC simulated annealing algorithm, relates to the field of power system combination optimization, and solves the technical problem that an existing FPGA-based Isin model solver adopts a traditional Monte Carlo method and is low in convergence precision and solution quality. The method comprises the following steps: receiving a question input by a user through an upper computer, and mapping the received question into a Hamiltonian parameter of an Isin model; the Hamiltonian parameter is transmitted to an FPGA through a communication interface, and a mixed GHMC sampler and a temperature scheduling module are arranged in the FPGA; the hybrid GHMC sampler carries out parallel solution on Hamiltonian parameters; and the temperature scheduling module gradually reduces the system temperature according to a preset annealing curve, and drives the Isin model to converge to a global optimal solution. The method is used in the power system combination optimization process.
Need to check novelty before this filing date? Find Prior Art