A High-Performance Stochastic Simulated Bifurcation Ising Machine

Tingting Zhang, Hongqiao Zhang, Zhengkun Yu, Siting Liu, Jie Han

Abstract: Ising model-based computers, or Ising machines, have recently emerged as high-performance solvers for combinatorial optimization problems (COPs). A simulated bifurcation (SB) Ising machine searches for the solution by solving pairs of differential equations related to the oscillator positions and momenta. It benefits from massive parallelism but suffers from high energy. As an unconventional computing paradigm, dynamic stochastic computing implements accumulation-based operations efficiently. By exploiting the advantages in algorithm and hardware codesign, this article proposes a high-performance stochastic SB machine (SSBM) with efficient hardware. To this end, we develop a stochastic SB (sSB) algorithm such that the multiply-and-accumulate (MAC) operation is converted to multiplexing and addition while the numerical integration is implemented by using signed stochastic integrators (SSIs). Specifically, the sSB stochastically ternarizes position values used for the MAC operation. Two types of SB cells are constructed. A stochastic computing SB cell contains two SSIs with a high area efficiency, while a binary-stochastic computing SB cell contains one binary integrator and one SSI with a reduced delay. Based on sSB, an SSBM is then built by using the proposed SB cells as the basic building block. The designs and syntheses of two SSBMs with 2000 fully connected spins require at least 10.62% smaller area than the state-of-the-art designs. It shows the potential of stochastic computing for SB to efficiently solve COPs.