Many probabilistic computing frameworks have been developed in recent years due to their potential as faster, energy-efficient alternatives to von Neumann computers for combinatorial optimization problems. In this work, we study the dynamics of a two-spin analog Ising computer implemented with superparamagnetic tunnel junctions (SMTJs). The operational-amplifier-based circuit features a polarity selection and a programmable gain parameter, allowing us to achieve both positive and negative coupling and perform simulated annealing if the gain is treated as inverse temperature. Experiments show that correlation between coupled SMTJs approaches 1 in the high-gain limit. Scaling of this design requires only trivial modifications to the circuit; however, scaling up to large networks of spins requires the development of SMTJs with enhanced properties, suggesting that a co-design approach between devices, architectures and algorithms is necessary.
Due to their interesting physical properties, myriad operational regimes, small size, and industrial fabrication maturity, magnetic tunnel junctions are uniquely suited for unlocking novel computing schemes for in-hardware neuromorphic computing. In this paper, we focus on the stochastic response of magnetic tunnel junctions, illustrating three different ways in which the probabilistic response of a device can be used to achieve useful neuromorphic computing power.
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