Integrated Hardware Annealing based on Langevin Dynamics for Ising Machines
2026-08-26 • Hardware Architecture
Hardware Architecture
AI summaryⓘ
The authors developed a new way to help Ising machines, which are special computers that solve tricky puzzles, avoid getting stuck in poor solutions. They used a method called Langevin dynamics, which adds a bit of randomness to help the machine find the best answer. Their design was tested on a chip simulation and showed better results, finding the optimal solution more often and faster than other current methods. This means their approach could make these special machines more efficient at solving difficult problems.
Ising machinescombinatorial optimizationLangevin dynamicshardware annealinglocal minimarandom noise65-nm CMOSchip-level simulationground stateenergy landscape
Authors
Yongchao Liu, Lianlong Sun, Michael Huang, Hui Wu
Abstract
Ising machines are non-von Neumann machines designed to solve combinatorial optimization problems (COP) by searching for the ground state, or the lowest energy configuration, within the Ising model. However, Ising machines often face the challenges of getting trapped in local minima due to the complex energy landscapes. Hardware annealing algorithms help mitigate this issue by using a probabilistic approach to steer the system toward the ground state. In this paper, we present a hardware annealing algorithm for Ising machines based on Langevin dynamics, a stochastic perturbation by random noise. Theoretical analysis, system-level design, and detailed circuit design are carried out. We evaluate the performance of the algorithm through chip-level simulation using a standard 65-nm CMOS technology to demonstrate the algorithm's efficacy. The results show that the proposed hardware annealing algorithm effectively guides the system to reach the ground state with a probability of 86.5%, significantly improving the solution quality by 97.5%. Further, we compare the algorithm with state-of-the-art hardware annealing methods through behavioral-level simulations, highlighting its improved solution quality alongside a 50% reduction in time-to-solution.