A Reversible Evolvable Network Architecture and Methodology to Overcome the Heat Generation Problem in Molecular Scale Brain Building

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{degaris:2002:gecco:lbp,
  title =        "A Reversible Evolvable Network Architecture and
                 Methodology to Overcome the Heat Generation Problem in
                 Molecular Scale Brain Building",
  author =       "Hugo {de Garis} and Jonathan Dinerstein and 
                 Ravichandra Sriram",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "83--90",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.iss.whu.edu.cn/degaris/papers/RENN.pdf",
  abstract =     "Today's irreversible computing style, in which bits of
                 information are routinely wiped out (e.g. a NAND gate
                 has 2 input bits, and only 1 output bit), cannot
                 continue. If Moore's Law remains valid until 2020, as
                 many commentators think, then the heat generated in
                 molecular scale circuits that Moore's Law will provide,
                 would be so intense that they will explode [Hall 1992].
                 To avoid such heat generation problems, it has been
                 known since the early 1970s [Bennet 1973] that the
                 secret to ``heatless computation'' is to compute
                 reversibly, i.e. not to destroy bits, by sending in the
                 input bit-string through a computer built from
                 reversible logic gates (e.g. Fredkin gates [Fredkin et
                 al 1982], to record the output answer and then send the
                 output bit-string backwards through the computer to
                 obtain the original input bit-string. This reversible
                 style of computing takes twice as long, but does not
                 destroy bits, hence does not generate heat. (Landauer's
                 principle states that the heat generated from
                 irreversible computing is derived from the destruction
                 of bits of information [Landauer 1961]). The first
                 author intends to build artificial brains over the
                 remaining 20 years of his active research career, by
                 evolving (neural) network modules directly in
                 electronics (at electronic speeds) in their 100,000s
                 and assembling them into artificial brains. In the next
                 10-20 years, electronic circuitry will reach molecular
                 scales; hence a conceptual problem needs to be faced.
                 How to make evolvable (neural) networks that are
                 reversible? This paper proposes a reversible evolvable
                 Boolean network architecture and methodology which, it
                 is hoped, will stimulate the evolvable hardware and
                 evolvable neural network research communities to devote
                 more effort towards solving this problem, which can
                 only accentuate as Moore's Law continues to bite.",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp",
}

Genetic Programming entries for Hugo de Garis Jonathan Dinerstein Ravichandra Sriram

Citations