485,430 research outputs found
Constraining Attacker Capabilities Through Actuator Saturation
For LTI control systems, we provide mathematical tools - in terms of Linear
Matrix Inequalities - for computing outer ellipsoidal bounds on the reachable
sets that attacks can induce in the system when they are subject to the
physical limits of the actuators. Next, for a given set of dangerous states,
states that (if reached) compromise the integrity or safe operation of the
system, we provide tools for designing new artificial limits on the actuators
(smaller than their physical bounds) such that the new ellipsoidal bounds (and
thus the new reachable sets) are as large as possible (in terms of volume)
while guaranteeing that the dangerous states are not reachable. This guarantees
that the new bounds cut as little as possible from the original reachable set
to minimize the loss of system performance. Computer simulations using a
platoon of vehicles are presented to illustrate the performance of our tools
Reservoir Computing Approach to Robust Computation using Unreliable Nanoscale Networks
As we approach the physical limits of CMOS technology, advances in materials
science and nanotechnology are making available a variety of unconventional
computing substrates that can potentially replace top-down-designed
silicon-based computing devices. Inherent stochasticity in the fabrication
process and nanometer scale of these substrates inevitably lead to design
variations, defects, faults, and noise in the resulting devices. A key
challenge is how to harness such devices to perform robust computation. We
propose reservoir computing as a solution. In reservoir computing, computation
takes place by translating the dynamics of an excited medium, called a
reservoir, into a desired output. This approach eliminates the need for
external control and redundancy, and the programming is done using a
closed-form regression problem on the output, which also allows concurrent
programming using a single device. Using a theoretical model, we show that both
regular and irregular reservoirs are intrinsically robust to structural noise
as they perform computation
Limits on Fundamental Limits to Computation
An indispensable part of our 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 last fifty years. Such Moore scaling now requires increasingly heroic
efforts, stimulating research in alternative hardware and stirring controversy.
To help evaluate emerging technologies and enrich our understanding of
integrated-circuit scaling, we review fundamental limits to computation: in
manufacturing, energy, physical space, design and verification effort, and
algorithms. To outline what is achievable in principle and in practice, we
recall how some limits were circumvented, compare loose and tight limits. We
also point out that engineering difficulties encountered by emerging
technologies may indicate yet-unknown limits.Comment: 15 pages, 4 figures, 1 tabl
Universal Limits on Computation
The physical limits to computation have been under active scrutiny over the
past decade or two, as theoretical investigations of the possible impact of
quantum mechanical processes on computing have begun to make contact with
realizable experimental configurations. We demonstrate here that the observed
acceleration of the Universe can produce a universal limit on the total amount
of information that can be stored and processed in the future, putting an
ultimate limit on future technology for any civilization, including a
time-limit on Moore's Law. The limits we derive are stringent, and include the
possibilities that the computing performed is either distributed or local. A
careful consideration of the effect of horizons on information processing is
necessary for this analysis, which suggests that the total amount of
information that can be processed by any observer is significantly less than
the Hawking-Bekenstein entropy associated with the existence of an event
horizon in an accelerating universe.Comment: 3 pages including eps figure, submitted to Phys. Rev. Lett; several
typos corrected, several references added, and a short discussion of w <-1
adde
On the Interpretation of Energy as the Rate of Quantum Computation
Over the last few decades, developments in the physical limits of computing
and quantum computing have increasingly taught us that it can be helpful to
think about physics itself in computational terms. For example, work over the
last decade has shown that the energy of a quantum system limits the rate at
which it can perform significant computational operations, and suggests that we
might validly interpret energy as in fact being the speed at which a physical
system is "computing," in some appropriate sense of the word. In this paper, we
explore the precise nature of this connection. Elementary results in quantum
theory show that the Hamiltonian energy of any quantum system corresponds
exactly to the angular velocity of state-vector rotation (defined in a certain
natural way) in Hilbert space, and also to the rate at which the state-vector's
components (in any basis) sweep out area in the complex plane. The total angle
traversed (or area swept out) corresponds to the action of the Hamiltonian
operator along the trajectory, and we can also consider it to be a measure of
the "amount of computational effort exerted" by the system, or effort for
short. For any specific quantum or classical computational operation, we can
(at least in principle) calculate its difficulty, defined as the minimum effort
required to perform that operation on a worst-case input state, and this in
turn determines the minimum time required for quantum systems to carry out that
operation on worst-case input states of a given energy. As examples, we
calculate the difficulty of some basic 1-bit and n-bit quantum and classical
operations in an simple unconstrained scenario.Comment: Revised to address reviewer comments. Corrects an error relating to
time-ordering, adds some additional references and discussion, shortened in a
few places. Figures now incorporated into tex
A Review on Software Architectures for Heterogeneous Platforms
The increasing demands for computing performance have been a reality
regardless of the requirements for smaller and more energy efficient devices.
Throughout the years, the strategy adopted by industry was to increase the
robustness of a single processor by increasing its clock frequency and mounting
more transistors so more calculations could be executed. However, it is known
that the physical limits of such processors are being reached, and one way to
fulfill such increasing computing demands has been to adopt a strategy based on
heterogeneous computing, i.e., using a heterogeneous platform containing more
than one type of processor. This way, different types of tasks can be executed
by processors that are specialized in them. Heterogeneous computing, however,
poses a number of challenges to software engineering, especially in the
architecture and deployment phases. In this paper, we conduct an empirical
study that aims at discovering the state-of-the-art in software architecture
for heterogeneous computing, with focus on deployment. We conduct a systematic
mapping study that retrieved 28 studies, which were critically assessed to
obtain an overview of the research field. We identified gaps and trends that
can be used by both researchers and practitioners as guides to further
investigate the topic
Hierarchical Composition of Memristive Networks for Real-Time Computing
Advances in materials science have led to physical instantiations of
self-assembled networks of memristive devices and demonstrations of their
computational capability through reservoir computing. Reservoir computing is an
approach that takes advantage of collective system dynamics for real-time
computing. A dynamical system, called a reservoir, is excited with a
time-varying signal and observations of its states are used to reconstruct a
desired output signal. However, such a monolithic assembly limits the
computational power due to signal interdependency and the resulting correlated
readouts. Here, we introduce an approach that hierarchically composes a set of
interconnected memristive networks into a larger reservoir. We use signal
amplification and restoration to reduce reservoir state correlation, which
improves the feature extraction from the input signals. Using the same number
of output signals, such a hierarchical composition of heterogeneous small
networks outperforms monolithic memristive networks by at least 20% on waveform
generation tasks. On the NARMA-10 task, we reduce the error by up to a factor
of 2 compared to homogeneous reservoirs with sigmoidal neurons, whereas single
memristive networks are unable to produce the correct result. Hierarchical
composition is key for solving more complex tasks with such novel nano-scale
hardware
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