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Domain-specific hardware to solve computationally hard optimization problemshas generated tremendous excitement recently. Here, we evaluate probabilisticbit (p-bit) based Ising Machines (IM), or p-computers with a benchmarkcombinatorial optimization problem, namely the 3-regular 3-XOR Satisfiability(3R3X). The 3R3X problem has a glassy energy landscape and it has recently beenused to benchmark various IMs and other solvers. We introduce a multiplexedarchitecture where p-computers emulate all-to-all (complete) graphfunctionality despite being interconnected in highly sparse networks, enablinghighly parallelized Gibbs sampling. We implement this architecture in FPGAs andshow that p-bit networks running an adaptive version of the powerful paralleltempering algorithm demonstrate competitive algorithmic and prefactoradvantages over alternative IMs by D-Wave, Toshiba and others. Scaled magneticnanodevice-based realizations of p-computers could lead to orders-of-magnitudefurther improvement according to experimentally established projections.

All-to-all reconfigurability with sparse Ising machines: the XORSAT challenge with p-bits