Hopfield vs Ising: A Comparison on the SoC FPAA

Physical computing techniques can efficiently solve combinatorial optimization problems and could outperform conventional digital techniques. This paper presents the first direct comparison of two physical quadratic optimization solvers: analog Hopfield and Ising networks. Both networks are built in...

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Bibliographic Details
Published inIEEE transactions on circuits and systems. I, Regular papers Vol. 71; no. 9; pp. 3999 - 4008
Main Authors Mathews, Pranav O., Hasler, Jennifer O.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Analog circuits
Associative memory
Circuits
Combinatorial analysis
Couplings
Digital techniques
Field programmable analog arrays
Field programmable gate arrays
FPAA
Hopfield
Hopfield neural networks
Initial conditions
Ising
Ising model
Networks
Neurons
Optimization
Oscillators
Virtual machine monitors
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Summary:Physical computing techniques can efficiently solve combinatorial optimization problems and could outperform conventional digital techniques. This paper presents the first direct comparison of two physical quadratic optimization solvers: analog Hopfield and Ising networks. Both networks are built in a large scale Field Programmable Analog Array (FPAA) and two NP-hard problems (max-cut and associative memory) are solved over various initial conditions and graphs. The convergence time, energy, power, and peripheral circuitry of the two networks is then compared where it is found that the Hopfield network outperforms the Ising networks in these test cases.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2024.3411407