An efficient synchronous-updating memristor-based Ising solver for combinatorial optimization
Technical digest - International Electron Devices Meeting/Technical digest, pp. 22.2.1–22.2.4
Abstract
Despite showing significant potential in solving combinatorial optimization problems, existing memristor-based solvers update node states asynchronously by performing matrix multiplication column-by-column, leaving the massive parallelism of the crossbar not fully exploited. In this work, we propose and experimentally demonstrate solving the optimization problems with a synchronous-updating memristor-based Ising solver, which is realized by a binary neural network-inspired updating algorithm and a physics-inspired annealing method. The newly proposed method saves more than 5x time and 35x energy consumption compared to the state-of-the-art mem-HNN for finding the optimal solution to a 60-node Max-cut problem.
Authors 6
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Affiliation as printed
The University of Hong Kong,Department of EEE,Hong Kong SAR,China
Department of EEE, The University of Hong Kong, Hong Kong SAR, China
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Affiliation as printed
The University of Hong Kong,Department of EEE,Hong Kong SAR,China
Department of EEE, The University of Hong Kong, Hong Kong SAR, China
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Hewlett-Packard (United States)
Affiliation as printed
Hewlett Packard Labs,Milpitas,CA,United States
Hewlett Packard Labs, Milpitas, CA, United States
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Hewlett-Packard (United States)
Affiliation as printed
Hewlett Packard Labs,Milpitas,CA,United States
Hewlett Packard Labs, Milpitas, CA, United States
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Affiliation as printed
Peter Grünberg Institut (PGI-14) and RWTH Aachen University,Germany
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Affiliation as printed
The University of Hong Kong,Department of EEE,Hong Kong SAR,China
Department of EEE, The University of Hong Kong, Hong Kong SAR, China
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