A Continuous-Time Ising Machine using Coupled Inverter Chains Featuring Fully-Parallel One-Shot Spin Updates

Unconventional computers based on superconducting qubits [1] and analog/digital hardware accelerators [2]–[5] have been developed and solved hard combinatorial optimization problems (COPs) with higher efficiency than classical computers by integrating the Ising models in hardware and observing their natural convergence behavior towards the lower Ising Hamiltonian (or energy). Ouantum annealer based on superconducting qubits [1] has demonstrated its capability of finding solutions to COPs using the quantum tunneling effect. However, the quantum computer requires operating at ultra-low temperatures (15-20mK), consumes excessive power (>10kW), and is not scalable yet to solve real-world problems. Recently, ASIC hardware accelerators [2]–[5] using low-cost CMOS technologies have implemented the Ising model by integrating many physical spins (i.e., computing units of the Ising model) to accelerate the finding of solutions to hard COPs. The hardware accelerators, so-called Ising machines, solved hard COPs much faster than classical computers and consumed several orders of magnitude lower energy. Nevertheless, they still have to overcome the fundamental scalability issues for solving large-scale COPs in the real world. The performance of existing discrete-time Ising machines has been limited by the exponentially increasing number of operation cycles when the problem size increases for the large-scale COPs. in addition, prior works have not been able to integrate massive random number generators (RNG), the essential building blocks of the Ising machines for finding better solutions, that could occupy a significant hardware footprint. Prior works have implemented them off-chip, or a few have been integrated on-chip and shared by many spins. This paper proposes a novel continuous-time analog Ising machine based on coupled inveiter chain circuits to address the scalability issues of the existing discrete-time Ising machines. Besides the benefits of the continuous-time operation, which significantly lowers the computing energy and latency, we introduce an equalization method for finding better solutions without RNGs.