Limits on fundamental limits to computation

By Igor L. Markov. Nature, 2014

484 citations recorded by the source corpus.

An indispensable part of our personal and working lives, computing has also become essential to industries and governments. Steady improvements in computer hardware have been supported by periodic doubling of transistor densities in integrated circuits over the past fifty years. Such Moore scaling now requires ever-increasing efforts, stimulating research in alternative hardware and stirring controversy. To help evaluate emerging technologies and increase our understanding of integrated-circuit scaling, here I review fundamental limits to computation in the areas of manufacturing, energy, physical space, design and verification effort, and algorithms. To outline what is achievable in principle and in practice, I recapitulate how some limits were circumvented, and compare loose and tight limits. Engineering difficulties encountered by emerging technologies may indicate yet unknown limits.

Subfields: Electrical and Electronic Engineering, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/ce64e7518ea31be76f54d9a804c99434c5141bbc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

In Hardware and Architecture

Citations in the corpus

Usage impact on data center electricity needs: A system dynamic forecasting model (2021, background): “…point at the efficiency gains by Moore ’ s law and improvements in data center infrastructure of about 20% annually [1], but the gains from Moore ’ s law flatten according to Shalf [2] and Markov [16], who predict Moore ’ s law to be fully out of order by 2021, a view shared by Shahidi [17] later.”
The future of computing beyond Moore’s Law (2020, background): “The classical technological driver that has underpinned Moore’s Law for the past 50 years is failing [3] and is anticipated to flatten by 2025, as shown in figure 2.”
Colloquium: Strong-field phenomena in periodic systems (2017, background): “As a result of the continued miniaturization of semiconductor integrated circuits (Ionescu and Riel, 2011; Taur and Ning, 2013), microelectronics has approached operating regimes where dissipation phenomena limit the rate of information processing (Markov, 2014; Pop, 2010).”
Quantum machine learning: a classical perspective (2017, background): “With the size of datasets constantly growing and Moore’s law coming to an end [3], we might soon reach a point where the current computational tools will”
Attojoule Optoelectronics for Low-Energy Information Processing and Communications (2016, background): “inside electronic machines is used to communicate; for example, even on silicon chips 50–80% of gates are for the “repeater” amplifiers in long interconnect lines on the chip [7], and information also has to be driven off chip [5], and over data links and networks [8], [9], at much greater energies ”