Approximate and Stochastic Ising Machines

The Ising model is useful in searching for (sub)-optimal solutions of combinatorial optimization problems (COPs). CMOS implementations of Ising model-based solvers, commonly referred to as Ising machines, provide reliable and accurate solutions with flexible and dense connectivity. However, they incur a significant hardware overhead. Approximate computing, as a low-power technique, offers a way to reduce hardware complexity, while stochastic computing is efficient in simulating the dynamics of the Ising model. The approximations introduced by these techniques may be beneficial in helping the system escape from local minima. In this talk, we discuss the potential of using approximate and stochastic computing to improve the performance of Ising machines.