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• W3048442213 abstract "Abstract In this work we demonstrate usage of the Restricted Boltzmann Machine (RBM) as a stochastic neural network capable of solving NP-Hard Combinatorial Optimization problems efficiently. By mapping the RBM onto a reconfigurable Field Programmable Gate Array (FPGA), we can effectively hardware accelerate the RBM's stochastic sampling algorithm. We benchmark the RBM against the DWave 2000Q Quantum Adiabatic Computer and the Optical Coherent Ising Machine on two such optimization problems: the MAX-CUT problem and the Sherrington-Kirkpatrick (SK) spin glass. The hardware accelerated RBM shows asymptotic scaling either similar or better than these other accelerators. This leads to 10 7 x and 10 5 x time to solution improvement compared to the DWave 2000Q on the MAX-CUT and SK problems respectively, along with a 150x and 1000x improvement compared to the Coherent Ising Machine annealer on those problems. By utilizing commodity hardware running at room temperature, the RBM shows potential for immediate and scalable use." @default.
• W3048442213 created "2020-08-18" @default.
• W3048442213 creator A5020302124 @default.
• W3048442213 creator A5042125443 @default.
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• W3048442213 date "2020-12-15" @default.
• W3048442213 modified "2023-09-26" @default.
• W3048442213 title "Ising Model Optimization Problems on a FPGA Accelerated Restricted Boltzmann Machine" @default.
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• W3048442213 doi "https://doi.org/10.21203/rs.3.rs-122016/v1" @default.
• W3048442213 hasPublicationYear "2020" @default.
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