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Entropy 2018, 20(10), 749; https://doi.org/10.3390/e20100749

Analysis of Heat Dissipation and Reliability in Information Erasure: A Gaussian Mixture Approach

1
Department of Mechanical Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
2
Department of Electrical and Computer Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
*
Author to whom correspondence should be addressed.
Received: 18 June 2018 / Revised: 20 September 2018 / Accepted: 28 September 2018 / Published: 30 September 2018
(This article belongs to the Special Issue Thermodynamics of Information Processing)
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Abstract

This article analyzes the effect of imperfections in physically realizable memory. Motivated by the realization of a bit as a Brownian particle within a double well potential, we investigate the energetics of an erasure protocol under a Gaussian mixture model. We obtain sharp quantitative entropy bounds that not only give rigorous justification for heuristics utilized in prior works, but also provide a guide toward the minimal scale at which an erasure protocol can be performed. We also compare the results obtained with the mean escape times from double wells to ensure reliability of the memory. The article quantifies the effect of overlap of two Gaussians on the the loss of interpretability of the state of a one bit memory, the required heat dissipated in partially successful erasures and reliability of information stored in a memory bit. View Full-Text
Keywords: information erasure; generalized Landauer’s principle; thermodynamics of information; reliability of a bit information erasure; generalized Landauer’s principle; thermodynamics of information; reliability of a bit
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Talukdar, S.; Bhaban, S.; Melbourne, J.; Salapaka, M. Analysis of Heat Dissipation and Reliability in Information Erasure: A Gaussian Mixture Approach. Entropy 2018, 20, 749.

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