  The ground state search of the Ising model can be used to solve many
combinatorial optimization problems. Under the current computer architecture,
an Ising ground state search algorithm suitable for hardware computing is
necessary for solving practical problems. Inspired by the potential energy
conversion of springs, we propose a point convolutional neural network
algorithm for ground state search based on spring vibration model, called
Spring-Ising Algorithm. Spring-Ising Algorithm regards the spin as a moving
mass point connected to a spring and establish the equation of motion for all
spins. Spring-Ising Algorithm can be mapped on the GPU or AI chips through the
basic structure of the neural network for fast and efficient parallel
computing. The algorithm has very productive results for solving the Ising
model and has been test in the recognized test benchmark K2000. The algorithm
introduces the concept of dynamic equilibrium to achieve a more detailed local
search by dynamically adjusting the weight of the Ising model in the spring
oscillation model. Finally, there is the simple hardware test speed evaluation.
Spring-Ising Algorithm can provide the possibility to calculate the Ising model
on a chip which focuses on accelerating neural network calculations.
