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A Boltzmann machine whose effective "temperature" can be dynamically "cooled"provides a stochastic neural network realization of simulated annealing, whichis an important metaheuristic for solving combinatorial or global optimizationproblems with broad applications in machine intelligence and operationsresearch. However, the hardware realization of the Boltzmann stochastic elementwith "cooling" capability has never been achieved within an individualsemiconductor device. Here we demonstrate a new memristive device concept basedon two-dimensional material heterostructures that enables this criticalstochastic element in a Boltzmann machine. The dynamic cooling effect insimulated annealing can be emulated in this multi-terminal memristive devicethrough electrostatic bias with sigmoidal thresholding distributions. We alsoshow that a machine-learning-based method is efficient for device-circuitco-design of the Boltzmann machine based on the stochastic memristor devices insimulated annealing. The experimental demonstrations of the tunable stochasticmemristors combined with the machine-learning-based device-circuitco-optimization approach for stochastic-memristor-based neural-network circuitschart a pathway for the efficient hardware realization of stochastic neuralnetworks with applications in a broad range of electronics and computingdisciplines.

Machine Learning Approach for Device-Circuit Co-Optimization of Stochastic-Memristive-Device-Based Boltzmann Machine