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Evolution of the Semiconductor Industry, and the Start of X Law

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Genetic Programming Theory and Practice XVIII

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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Abstract

In this paper, we explore the use of evolutionary concepts to predict what-comes-next for the Semiconductor Industry. At its core, evolution is the transition of information. Understanding what causes the transitions paves the way to potentially creating a predictive model for the industry. Prediction is one of the essential functions of research; it is challenging to get right; it is of paramount importance when it comes to determining the next commercial objective and often depends on a single change. The most critical part of the prediction is to explore the components that form the landscape of potential outcomes. With these outcomes, we can decide what careers to take, what areas to dedicate resources towards and further out as a possible method to increase revenue. The Semiconductor Industry  is a complex ecosystem, where many adjacent industries rely on its continued advancements. The human appetite to consume more data puts pressure on the industry. Consumption drives three technology vectors, namely storage, compute, and communication. Under this premise, two thoughts lead to this paper. Firstly, the End of Moore’s Law (EoML) [33], where transistor density growth slows down over time. Either due to costs or technology constraints (thermal and energy restrictions). These factors mean that traditional iterative methods, adopted by the Semiconductor Industry, may fail to satisfy future data demands. Secondly, the quote by Leonard Adleman “Evolution is not the story of life; it is the story of compute” [2], where essentially evolution is used as a method to understand future advancements. Understanding a landscape and its parameterization could lead to a predictive model for the Semiconductor Industry. The plethora of future evolutionary steps available means we should probably discard focusing on EoML and shift our attention to finding the next new law for the industry. The new law is the Start of X Law, where X symbolizes a new beginning. Evolutionary principles show that co-operation and some form of altruism may be the only methods to achieve these forward steps. Future choices end up being a balancing act between conflicting ideas due to the multi-objective nature of the overall requirements.

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Acknowledgements

Each paper is only as good as the reviewers. I wish to personally thank the following people: Andrew Loats, Lee Smith, Paul Gleichauf, Greg Yeric, Karl Fezer, Joseph Fernando, Tim Street, Stuart Card, and Gary Carpenter for their participation and thoughts. Lastly, I would like to specially thank Stephen Freeland for a great talk, and initial strange discussion, on the idea of mapping the Semiconductor Industry to Evolutionary Biology.

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  1. Arm Ltd., Seattle, Washington, USA

    Andrew N. Sloss

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  1. Andrew N. Sloss
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Correspondence to Andrew N. Sloss .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Tecnológico Nacional de México/IT de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan Winkler

  4. Evolution Enterprise, Ann Arbor, MI, USA

    Bill Worzel

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Sloss, A.N. (2022). Evolution of the Semiconductor Industry, and the Start of X Law. In: Banzhaf, W., Trujillo, L., Winkler, S., Worzel, B. (eds) Genetic Programming Theory and Practice XVIII. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-16-8113-4_11

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  • DOI: https://doi.org/10.1007/978-981-16-8113-4_11

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  • Online ISBN: 978-981-16-8113-4

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