24.3 20k-spin Ising chip for combinational optimization problem with CMOS annealing

M. Yamaoka, C. Yoshimura, Masato Hayashi, Takuya Okuyama, Hidetaka Aoki, Hiroyuki Mizuno
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引用次数: 89

Abstract

In the near future, the performance growth of Neumann-architecture computers will slow down due to the end of semiconductor scaling. Presently a new computing paradigm, so-called natural computing, which maps problems to physical models and solves the problem by its own convergence property, is expected. The analog computer using superconductivity from D-Wave [1] is one of those computers. A neuron chip [2] is also one of them. We proposed a CMOS-type Ising computer [3]. The Ising computer maps problems to an Ising model, a model to express the behavior of magnetic spins (the upper left diagram in Fig. 24.3.1), and solves the problems by ground-state search operations. The energy of the system is expressed by the formula in the diagram. Computing flows are expressed in the lower flow chart in Fig. 24.3.1. In the conventional Neumann architecture, the problem is sequentially and repeatedly calculated, and therefore, the number of computing steps drastically increases as the problem size grows. In the Ising computer, in the first step, the problem is mapped to the Ising model. In the next steps, an annealing operation, the ground-state search by interactions between spins, are activated and the state transitions to the ground state where the energy of the system is minimized. The interacting operation between spins is decided by the interaction coefficients, which are set to each connection. Here, the configuration of the interaction coefficients is decided by the problem, and therefore, the interaction coefficients are equivalent to the programming in the conventional computing paradigm. The ground state corresponds to the solution of the original problem, and the solution is acquired by observing the ground state. The interactions for the annealing are performed in parallel, and the necessary steps for the annealing are smaller than that used by a sequential computing, Neumann architecture. As the table in Fig. 24.3.1, our Ising computer uses CMOS circuits to express the Ising model, and acquires the scalability and operation at room temperature.
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24.3 20k自旋Ising芯片与CMOS退火的组合优化问题
在不久的将来,由于半导体缩放的结束,诺伊曼架构计算机的性能增长将放缓。目前,人们期待一种新的计算范式,即所谓的自然计算,它将问题映射到物理模型,并通过其自身的收敛性来解决问题。D-Wave[1]中使用超导的模拟计算机就是其中之一。神经元芯片[2]也是其中之一。我们提出了一种cmos型Ising计算机[3]。Ising计算机将问题映射到表达磁自旋行为的Ising模型(图24.3.1左上图),并通过基态搜索操作解决问题。系统的能量用图中的公式表示。计算流程如图24.3.1下流程图所示。在传统的诺伊曼体系结构中,问题是顺序和重复计算的,因此,随着问题规模的增长,计算步骤的数量急剧增加。在伊辛计算机中,第一步,问题被映射到伊辛模型。在接下来的步骤中,退火操作,通过自旋之间的相互作用来搜索基态,被激活,状态转换到系统能量最小的基态。自旋之间的相互作用由相互作用系数决定,相互作用系数设置为每个连接。在这里,交互系数的配置由问题决定,因此,交互系数等同于传统计算范式中的编程。基态对应原问题的解,通过观测基态得到解。退火的相互作用是并行执行的,退火的必要步骤比顺序计算所使用的步骤要小,诺伊曼体系结构。如图24.3.1所示,我们的Ising计算机使用CMOS电路来表达Ising模型,并获得了在室温下的可扩展性和可操作性。
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