Superparamagnetic tunnel junctions (SMTJs) and spin-torque nano-oscillators (STNOs) show promise for use in energy-efficient unconventional computing schemes based on stochastic information encodings, operating from nanosecond to microsecond time scales. We demonstrate electrical coupling of SMTJs for emulating neuro-synaptic connections and leverage the phase dynamics of STNOs for innovative approaches to unbiased random number generation, with the potential to mimic fast stochastic binary neurons, paving the way for low-energy, hardware-based stochastic neural networks.
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.
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