All-to-all reconfigurability with sparse Ising machines: the XORSAT challenge with p-bits
CoRR(2023)
摘要
Domain-specific hardware to solve computationally hard optimization problems
has generated tremendous excitement recently. Here, we evaluate probabilistic
bit (p-bit) based Ising Machines (IM), or p-computers with a benchmark
combinatorial optimization problem, namely the 3-regular 3-XOR Satisfiability
(3R3X). The 3R3X problem has a glassy energy landscape and it has recently been
used to benchmark various IMs and other solvers. We introduce a multiplexed
architecture where p-computers emulate all-to-all (complete) graph
functionality despite being interconnected in highly sparse networks, enabling
highly parallelized Gibbs sampling. We implement this architecture in FPGAs and
show that p-bit networks running an adaptive version of the powerful parallel
tempering algorithm demonstrate competitive algorithmic and prefactor
advantages over alternative IMs by D-Wave, Toshiba and others. Scaled magnetic
nanodevice-based realizations of p-computers could lead to orders-of-magnitude
further improvement according to experimentally established projections.
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