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Probabilistic computing using random number generators (RNGs) can leveragethe inherent stochasticity of nanodevices for system-level benefits. Themagnetic tunnel junction (MTJ) has been studied as an RNG due to itsthermally-driven magnetization dynamics, often using spin transfer torque (STT)current amplitude to control the random switching of the MTJ free layermagnetization, here called the stochastic write method. There are additionalknobs to control the MTJ-RNG, including voltage-controlled magnetic anisotropy(VCMA) and spin orbit torque (SOT), and there is need to systematically studyand compared these methods. We build an analytical model of the MTJ tocharacterize using VCMA and SOT to generate random bit streams. The resultsshow that both methods produce high quality, uniformly distributed bitstreams.Biasing the bitstreams using either STT current or an applied magnetic fieldshows an approximately sigmoidal distribution vs. bias amplitude for both VCMAand SOT, compared to less sigmoidal for stochastic write for the sameconditions. The energy consumption per sample is calculated to be 0.1 pJ (SOT),1 pJ (stochastic write), and 20 pJ (VCMA), revealing the potential energybenefit of using SOT and showing using VCMA may require higher dampingmaterials. The generated bitstreams are then applied to two example tasks:generating an arbitrary probability distribution using the Hidden CorrelationBernoulli Coins method and using the MTJ-RNGs as stochastic neurons to performsimulated annealing in a Boltzmann machine, where both VCMA and SOT methodsshow the ability to effectively minimize the system energy with small delay andlow energy. These results show the flexibility of the MTJ as a true RNG andelucidate design parameters for optimizing the device operation forapplications.

Random Bitstream Generation using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions