Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine

Xiaodong Yan, Jiahui Ma, Tong Wu, Aoyang Zhang, Jiangbin Wu, Matthew Chin, Zhihan Zhang, Madan Dubey, Wei Wu, Mike Shuo-Wei Chen, Jing Guo and Han Wang ()
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Xiaodong Yan: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California
Jiahui Ma: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California
Tong Wu: University of Florida
Aoyang Zhang: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California
Jiangbin Wu: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California
Matthew Chin: Sensors and Electron Devices Directorate, U.S. Army Research Laboratory
Zhihan Zhang: School of Electrical and Computer Engineering, Georgia Institute of Technology
Madan Dubey: Sensors and Electron Devices Directorate, U.S. Army Research Laboratory
Wei Wu: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California
Mike Shuo-Wei Chen: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California
Jing Guo: University of Florida
Han Wang: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California

Nature Communications, 2021, vol. 12, issue 1, 1-8

Abstract: Abstract Neuromorphic hardware implementation of Boltzmann Machine using a network of stochastic neurons can allow non-deterministic polynomial-time (NP) hard combinatorial optimization problems to be efficiently solved. Efficient implementation of such Boltzmann Machine with simulated annealing desires the statistical parameters of the stochastic neurons to be dynamically tunable, however, there has been limited research on stochastic semiconductor devices with controllable statistical distributions. Here, we demonstrate a reconfigurable tin oxide (SnOx)/molybdenum disulfide (MoS2) heterogeneous memristive device that can realize tunable stochastic dynamics in its output sampling characteristics. The device can sample exponential-class sigmoidal distributions analogous to the Fermi-Dirac distribution of physical systems with quantitatively defined tunable “temperature” effect. A BM composed of these tunable stochastic neuron devices, which can enable simulated annealing with designed “cooling” strategies, is conducted to solve the MAX-SAT, a representative in NP-hard combinatorial optimization problems. Quantitative insights into the effect of different “cooling” strategies on improving the BM optimization process efficiency are also provided.

Date: 2021
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DOI: 10.1038/s41467-021-26012-5

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