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Graphical probabilistic circuit models of stochastic computing are morepowerful than the predominant deep learning models, but also have moredemanding requirements. For example, they require "programmable stochasticity",e.g. generating two random binary bit streams with tunable amount ofcorrelation between the corresponding bits in the two streams. Electronicimplementation of such a system would call for several components leaving alarge footprint on a chip and dissipating excessive amount of energy. Here, weshow an elegant implementation with just two dipole-coupled magneto-tunnelingjunctions (MTJ), with magnetostrictive soft layers, fabricated on apiezoelectric film. The resistance states of the two MTJs (high or low) encodethe bits in the two streams. The first MTJ is driven to a random resistancestate via a current or voltage generating spin transfer torque and/or voltagecontrolled magnetic anisotropy, while the second MTJ's resistance state isdetermined solely by dipole coupling with the first. The effect of dipolecoupling can be varied with local strain applied to the second MTJ with a localvoltage (~0.2 V) and that varies the correlation between the resistance statesof the two MTJs and hence between the bits in the two streams (from 0% to100%). This paradigm can be extended to arbitrary number of bit streams.

A Nanomagnetic Voltage-Tunable Correlation Generator between Two Random Bit Streams for Stochastic Computing