105,266 research outputs found
Thermodynamic cost of reversible computing
Since reversible computing requires preservation of all information
throughout the entire computational process, this implies that all errors that
appear as a result of the interaction of the information-carrying system with
uncontrolled degrees of freedom must be corrected. But this can only be done at
the expense of an increase in the entropy of the environment corresponding to
the dissipation, in the form of heat, of the ``noisy'' part of the system's
energy.
This paper gives an expression of that energy in terms of the effective noise
temperature, and analyzes the relationship between the energy dissipation rate
and the rate of computation. Finally, a generalized Clausius principle based on
the concept of effective temperature is presented.Comment: 5 pages; added two paragraphs and fixed a number of typo
Thermodynamic Computing
The hardware and software foundations laid in the first half of the 20th
Century enabled the computing technologies that have transformed the world, but
these foundations are now under siege. The current computing paradigm, which is
the foundation of much of the current standards of living that we now enjoy,
faces fundamental limitations that are evident from several perspectives. In
terms of hardware, devices have become so small that we are struggling to
eliminate the effects of thermodynamic fluctuations, which are unavoidable at
the nanometer scale. In terms of software, our ability to imagine and program
effective computational abstractions and implementations are clearly challenged
in complex domains. In terms of systems, currently five percent of the power
generated in the US is used to run computing systems - this astonishing figure
is neither ecologically sustainable nor economically scalable. Economically,
the cost of building next-generation semiconductor fabrication plants has
soared past $10 billion. All of these difficulties - device scaling, software
complexity, adaptability, energy consumption, and fabrication economics -
indicate that the current computing paradigm has matured and that continued
improvements along this path will be limited. If technological progress is to
continue and corresponding social and economic benefits are to continue to
accrue, computing must become much more capable, energy efficient, and
affordable. We propose that progress in computing can continue under a united,
physically grounded, computational paradigm centered on thermodynamics. Herein
we propose a research agenda to extend these thermodynamic foundations into
complex, non-equilibrium, self-organizing systems and apply them holistically
to future computing systems that will harness nature's innate computational
capacity. We call this type of computing "Thermodynamic Computing" or TC.Comment: A Computing Community Consortium (CCC) workshop report, 36 page
A method for computing chemical-equilibrium compositions of reacting-gas mixtures by reduction to a single iteration equation
Computing equilibrium chemical composition and thermodynamic properties of reacting gas mixtures by reduction to single iterative equatio
Computing the Absolute Gibbs Free Energy in Atomistic Simulations: Applications to Defects in Solids
The Gibbs free energy is the fundamental thermodynamic potential underlying
the relative stability of different states of matter under constant-pressure
conditions. However, computing this quantity from atomic-scale simulations is
far from trivial. As a consequence, all too often the potential energy of the
system is used as a proxy, overlooking entropic and anharmonic effects. Here we
discuss a combination of different thermodynamic integration routes to obtain
the absolute Gibbs free energy of a solid system starting from a harmonic
reference state. This approach enables the direct comparison between the free
energy of different structures, circumventing the need to sample the transition
paths between them. We showcase this thermodynamic integration scheme by
computing the Gibbs free energy associated with a vacancy in BCC iron, and the
intrinsic stacking fault free energy of nickel. These examples highlight the
pitfalls of estimating the free energy of crystallographic defects only using
the minimum potential energy, which overestimates the vacancy free energy by
60% and the stacking-fault energy by almost 300% at temperatures close to the
melting point
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