6 research outputs found
Fundamental energy cost of finite-time computing
The fundamental energy cost of irreversible computing is given by the
Landauer bound of ~/bit. However, this limit is only achievable for
infinite-time processes. We here determine the fundamental energy cost of
finite-time irreversible computing \er{within the framework of nonequilibrium
thermodynamics}. Comparing the lower bounds of energy required by ideal serial
and parallel computers to solve a problem of a given size in a given finite
time, we find that the energy cost of a serial computer fundamentally diverges
with increasing problem size, whereas that of a parallel computer can stay
close to the Landauer limit. We discuss the implications of this result in the
context of current technology, and for different degrees of parallelization and
amounts of overhead. Our findings provide a physical basis for the design of
energy efficient computers
Nonequilibrium control of thermal and mechanical changes in a levitated system
Fluctuation theorems are fundamental extensions of the second law of
thermodynamics for small nonequilibrium systems. While work and heat are
equally important forms of energy exchange, fluctuation relations have not been
experimentally assessed for the generic situation of simultaneous mechanical
and thermal changes. Thermal driving is indeed generally slow and more
difficult to realize than mechanical driving. We here use feedback cooling
techniques to implement fast and controlled temperature variations of an
underdamped levitated microparticle that are one order of magnitude faster than
the equilibration time. Combining mechanical and thermal control, we verify the
validity of a fluctuation theorem that accounts for both contributions, well
beyond the range of linear response theory. Our system allows the investigation
of general far-from-equilibrium processes in microscopic systems that involve
fast mechanical and thermal changes at the same time
Solving the subset sum problem with a nonideal biological computer
We consider the solution of the subset sum problem based on a parallel
computer consisting of self-propelled biological agents moving in a
nanostructured network that encodes the NP-complete task in its geometry. We
develop an approximate analytical method to analyze the effects of small errors
in the nonideal junctions composing the computing network by using a Gaussian
confidence interval approximation of the multinomial distribution. We
concretely evaluate the probability distribution for error-induced paths and
determine the minimal number of agents required to obtain a proper solution. We
finally validate our theoretical results with exact numerical simulations of
the subset sum problem for different set sizes and error probabilities
Fundamental energy cost of finite-time parallelizable computing
The fundamental energy cost of irreversible computing is given by the Landauer bound of kTln 2 /bit, where k is the Boltzmann constant and T is the temperature in Kelvin. However, this limit is only achievable for infinite-time processes. We here determine the fundamental energy cost of finite-time parallelizable computing within the framework of nonequilibrium thermodynamics. We apply these results to quantify the energetic advantage of parallel computing over serial computing. We find that the energy cost per operation of a parallel computer can be kept close to the Landauer limit even for large problem sizes, whereas that of a serial computer fundamentally diverges. We analyze, in particular, the effects of different degrees of parallelization and amounts of overhead, as well as the influence of non-ideal electronic hardware. We further discuss their implications in the context of current technology. Our findings provide a physical basis for the design of energy-efficient computers