5,306 research outputs found
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
Stochastic thermodynamics of computation
One of the major resource requirements of computers - ranging from biological
cells to human brains to high-performance (engineered) computers - is the
energy used to run them. Those costs of performing a computation have long been
a focus of research in physics, going back to the early work of Landauer. One
of the most prominent aspects of computers is that they are inherently
nonequilibrium systems. However, the early research was done when
nonequilibrium statistical physics was in its infancy, which meant the work was
formulated in terms of equilibrium statistical physics. Since then there have
been major breakthroughs in nonequilibrium statistical physics, which are
allowing us to investigate the myriad aspects of the relationship between
statistical physics and computation, extending well beyond the issue of how
much work is required to erase a bit. In this paper I review some of this
recent work on the `stochastic thermodynamics of computation'. After reviewing
the salient parts of information theory, computer science theory, and
stochastic thermodynamics, I summarize what has been learned about the entropic
costs of performing a broad range of computations, extending from bit erasure
to loop-free circuits to logically reversible circuits to information ratchets
to Turing machines. These results reveal new, challenging engineering problems
for how to design computers to have minimal thermodynamic costs. They also
allow us to start to combine computer science theory and stochastic
thermodynamics at a foundational level, thereby expanding both.Comment: 111 pages, no figures. arXiv admin note: text overlap with
arXiv:1901.0038
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Heat Dissipation Bounds for Nanocomputing: Methodology and Applications
Heat dissipation is a critical challenge facing the realization of emerging nanocomputing technologies. There are different components of this dissipation, and a part of it comes from the unavoidable cost of implementing logically irreversible operations. This stems from the fact that information is physical and manipulating it irreversibly requires energy. The unavoidable dissipative cost of losing information irreversibly fixes the fundamental limit on the minimum energy cost for computational strategies that utilize ubiquitous irreversible information processing.
A relation between the amount of irreversible information loss in a circuit and the associated energy dissipation was formulated by Landauer\u27s Principle in a technology-independent form. In a computing circuit, in addition to the nformation-theoretic dissipation, other physical processes that take place in association with irreversible information loss may also have an unavoidable thermodynamic cost that originates from the structure and operation of the circuit. In conventional CMOS circuits such unavoidable costs constitute only a minute fraction of the total power budget, however, in nanocircuits, it may be of critical significance due to the high density and operation speeds required. The lower bounds on energy, when obtained by considering the irreversible information cost as well as unavoidable costs associated with the operation of the underlying computing paradigm, may provide insight into the fundamental limitations of emerging technologies. This motivates us to study the problem of determining heat dissipation of computation in a way that reveals fundamental lower bounds on the energy cost for circuits realized in new computing paradigms.
In this work, we propose a physical-information-theoretic methodology that enables us to obtain such bounds for the minimum energy requirements of computation for concrete circuits realized within specific paradigms, and illustrate its application via prominent nanacomputing proposals. We begin by introducing the unavoidable heat dissipation problem and emphasize the significance of limitations it imposes on emerging technologies. We present the methodology developed to obtain the lower bounds on the unavoidable dissipation cost of computation for nanoelectronic circuits. We demonstrate our methodology via its application to various non-transistor-based (e.g. QCA) and transistor-based (e.g. NASIC) nanocomputing circuits. We also employ two CMOS circuits, in order to provide further insight into the application of our methodology by using this well-known conventional paradigm. We expand our methodology to modularize the dissipation analysis for QCA and NASIC paradigms, and discuss prospects for automation. We also revisit key concepts in thermodynamics of computation by focusing on the criticisms raised against the validity of Landauer\u27s Principle. We address these arguments and discuss their implications for our methodology. We conclude by elaborating possible directions towards which this work can be expanded
Perceptual lossless medical image coding
A novel perceptually lossless coder is presented for the compression of medical images. Built on the JPEG 2000 coding framework, the heart of the proposed coder is a visual pruning function, embedded with an advanced human vision model to identify and to remove visually insignificant/irrelevant information. The proposed coder offers the advantages of simplicity and modularity with bit-stream compliance. Current results have shown superior compression ratio gains over that of its information lossless counterparts without any visible distortion. In addition, a case study consisting of 31 medical experts has shown that no perceivable difference of statistical significance exists between the original images and the images compressed by the proposed coder
Radiation Hardness Assurance: Evolving for NewSpace
During the past decade, numerous small satellites have been launched into space, with dramatically expanded dependence on advanced commercial-off-the-shelf (COTS) technologies and systems required for mission success. While the radiation effects vulnerabilities of small satellites are the same as those of their larger, traditional relatives, revised approaches are needed for risk management because of differences in technical requirements and programmatic resources. While moving to COTS components and systems may reduce direct costs and procurement lead times, it undermines many cost-reduction strategies used for conventional radiation hardness assurance (RHA). Limited resources are accompanied by a lack of radiation testing and analysis, which can pose significant risksor worse, be neglected altogether. Small satellites have benefited from short mission durations in low Earth orbits with respect to their radiation response, but as mission objectives grow and become reliant on advanced technologies operating for longer and in harsher environments, requirements need to reflect the changing scope without hindering developers that provide new capabilities
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
Detection of amblyopia utilizing generated retinal reflexes
Investigation confirmed that GRR images can be consistently obtained and that these images contain information required to detect the optical inequality of one eye compared to the fellow eye. Digital analyses, electro-optical analyses, and trained observers were used to evaluate the GRR images. Two and three dimensional plots were made from the digital analyses results. These plotted data greatly enhanced the GRR image content, and it was possible for nontrained observers to correctly identify normal vs abnormal ocular status by viewing the plots. Based upon the criteria of detecting equality or inequality of ocular status of a person's eyes, the trained observer correctly identified the ocular status of 90% of the 232 persons who participated in this program
Physical Foundations of Landauer's Principle
We review the physical foundations of Landauer's Principle, which relates the
loss of information from a computational process to an increase in
thermodynamic entropy. Despite the long history of the Principle, its
fundamental rationale and proper interpretation remain frequently
misunderstood. Contrary to some misinterpretations of the Principle, the mere
transfer of entropy between computational and non-computational subsystems can
occur in a thermodynamically reversible way without increasing total entropy.
However, Landauer's Principle is not about general entropy transfers; rather,
it more specifically concerns the ejection of (all or part of) some correlated
information from a controlled, digital form (e.g., a computed bit) to an
uncontrolled, non-computational form, i.e., as part of a thermal environment.
Any uncontrolled thermal system will, by definition, continually re-randomize
the physical information in its thermal state, from our perspective as
observers who cannot predict the exact dynamical evolution of the microstates
of such environments. Thus, any correlations involving information that is
ejected into and subsequently thermalized by the environment will be lost from
our perspective, resulting directly in an irreversible increase in total
entropy. Avoiding the ejection and thermalization of correlated computational
information motivates the reversible computing paradigm, although the
requirements for computations to be thermodynamically reversible are less
restrictive than frequently described, particularly in the case of stochastic
computational operations. There are interesting possibilities for the design of
computational processes that utilize stochastic, many-to-one computational
operations while nevertheless avoiding net entropy increase that remain to be
fully explored.Comment: 42 pages, 15 figures, extended postprint of a paper published in the
10th Conf. on Reversible Computation (RC18), Leicester, UK, Sep. 201
A walk in the statistical mechanical formulation of neural networks
Neural networks are nowadays both powerful operational tools (e.g., for
pattern recognition, data mining, error correction codes) and complex
theoretical models on the focus of scientific investigation. As for the
research branch, neural networks are handled and studied by psychologists,
neurobiologists, engineers, mathematicians and theoretical physicists. In
particular, in theoretical physics, the key instrument for the quantitative
analysis of neural networks is statistical mechanics. From this perspective,
here, we first review attractor networks: starting from ferromagnets and
spin-glass models, we discuss the underlying philosophy and we recover the
strand paved by Hopfield, Amit-Gutfreund-Sompolinky. One step forward, we
highlight the structural equivalence between Hopfield networks (modeling
retrieval) and Boltzmann machines (modeling learning), hence realizing a deep
bridge linking two inseparable aspects of biological and robotic spontaneous
cognition. As a sideline, in this walk we derive two alternative (with respect
to the original Hebb proposal) ways to recover the Hebbian paradigm, stemming
from ferromagnets and from spin-glasses, respectively. Further, as these notes
are thought of for an Engineering audience, we highlight also the mappings
between ferromagnets and operational amplifiers and between antiferromagnets
and flip-flops (as neural networks -built by op-amp and flip-flops- are
particular spin-glasses and the latter are indeed combinations of ferromagnets
and antiferromagnets), hoping that such a bridge plays as a concrete
prescription to capture the beauty of robotics from the statistical mechanical
perspective.Comment: Contribute to the proceeding of the conference: NCTA 2014. Contains
12 pages,7 figure
A 3D Framework for Characterizing Microstructure Evolution of Li-Ion Batteries
Lithium-ion batteries are commonly found in many modern consumer devices, ranging from portable computers and mobile phones to hybrid- and fully-electric vehicles. While improving efficiencies and increasing reliabilities are of critical importance for increasing market adoption of the technology, research on these topics is, to date, largely restricted to empirical observations and computational simulations. In the present study, it is proposed to use the modern technique of X-ray microscopy to characterize a sample of commercial 18650 cylindrical Li-ion batteries in both their pristine and aged states. By coupling this approach with 3D and 4D data analysis techniques, the present study aimed to create a research framework for characterizing the microstructure evolution leading to capacity fade in a commercial battery. The results indicated the unique capabilities of the microscopy technique to observe the evolution of these batteries under aging conditions, successfully developing a workflow for future research studies
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