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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Sloss:2021:GPTP,

• author = "Andrew N. Sloss",
• title = "Evolution of the Semiconductor Industry, and the Start of {X} Law",
• booktitle = "Genetic Programming Theory and Practice XVIII",
• year = "2021",
• editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel",
• series = "Genetic and Evolutionary Computation",
• pages = "197--209",
• address = "East Lansing, USA",
• month = "19-21 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-981-16-8112-7",
• DOI = "doi:10.1007/978-981-16-8113-4_11",
• abstract = "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 Moores 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 parametrisation 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.",
• notes = "Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022",

}

Genetic Programming entries for Andrew N Sloss

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