509,726 research outputs found
Study and development of innovative strategies for energy-efficient cross-layer design of digital VLSI systems based on Approximate Computing
The increasing demand on requirements for high performance and energy efficiency in modern digital systems has led to the research of new design approaches that are able to go beyond the established energy-performance tradeoff. Looking at scientific literature, the Approximate Computing paradigm has been particularly prolific. Many applications in the domain of signal processing, multimedia, computer vision, machine learning are known to be particularly resilient to errors occurring on their input data and during computation, producing outputs that, although degraded, are still largely acceptable from the point of view of quality. The Approximate Computing design paradigm leverages the characteristics of this group of applications to develop circuits, architectures, algorithms that, by relaxing design constraints, perform their computations in an approximate or inexact manner reducing energy consumption. This PhD research aims to explore the design of hardware/software architectures based on Approximate Computing techniques, filling the gap in literature regarding effective applicability and deriving a systematic methodology to characterize its benefits and tradeoffs. The main contributions of this work are: -the introduction of approximate memory management inside the Linux OS, allowing dynamic allocation and de-allocation of approximate memory at user level, as for normal exact memory; - the development of an emulation environment for platforms with approximate memory units, where faults are injected during the simulation based on models that reproduce the effects on memory cells of circuital and architectural techniques for approximate memories; -the implementation and analysis of the impact of approximate memory hardware on real applications: the H.264 video encoder, internally modified to allocate selected data buffers in approximate memory, and signal processing applications (digital filter) using approximate memory for input/output buffers and tap registers; -the development of a fully reconfigurable and combinatorial floating point unit, which can work with reduced precision formats
GLM permutation - nonparametric inference for arbitrary general linear models
Introduction: Permutation methods are finding growing use in neuroimag-
ing data analyses (e.g. randomise in FSL, SnPM in SPM,
XBAMM/BAMM/CAMBA, etc). These methods provide ex-
act control of false positives, make only weak assumptions, and
allow nonstandard types of statistics (e.g. smoothed variance t-
test). With fast and inexpensive computing, there would seem
few reasons not to use nonparametric methods.
A significant limitation of these methods, however, is the lack of
flexibility with respect to the experimental design and nuisance
variables. Each specific design dictates the type of exchange-
ability of null data, and hence how to permute. Nuisance effects
(e.g. age) render data non-exchangeable even when the effect of
interest is null. Hence, even something as simple as ANCOVA
has no exact permutation test.
Recently there has been an active literature on approximate–
but accurate–permutation tests for 2-variable regression, one
effect of interest, one nuisance (see review by Anderson &
Robinson [1]). Here we extend and evaluate these methods
for use with an arbitrary General Linear Model (GLM)
MDP-Based Scheduling Design for Mobile-Edge Computing Systems with Random User Arrival
In this paper, we investigate the scheduling design of a mobile-edge
computing (MEC) system, where the random arrival of mobile devices with
computation tasks in both spatial and temporal domains is considered. The
binary computation offloading model is adopted. Every task is indivisible and
can be computed at either the mobile device or the MEC server. We formulate the
optimization of task offloading decision, uplink transmission device selection
and power allocation in all the frames as an infinite-horizon Markov decision
process (MDP). Due to the uncertainty in device number and location,
conventional approximate MDP approaches to addressing the curse of
dimensionality cannot be applied. A novel low-complexity sub-optimal solution
framework is then proposed. We first introduce a baseline scheduling policy,
whose value function can be derived analytically. Then, one-step policy
iteration is adopted to obtain a sub-optimal scheduling policy whose
performance can be bounded analytically. Simulation results show that the gain
of the sub-optimal policy over various benchmarks is significant.Comment: 6 pages, 3 figures; accepted by Globecom 2019; title changed to
better describe the work, introduction condensed, typos correcte
Energy-Efficient Approximate Least Squares Accelerator:A Case Study of Radio Astronomy Calibration Processing
Approximate computing allows the introduction of inaccuracy in the computation for cost savings, such as energy consumption, chip-area, and latency. Targeting energy efficiency, approximate designs for multipliers, adders, and multiply-accumulate (MAC) have been extensively investigated in the past decade. However, accelerator designs for relatively bigger architectures have been of less attention yet. The Least Squares (LS) algorithm is widely used in digital signal processing applications, e.g., image reconstruction. This work proposes a novel LS accelerator design based on a heterogeneous architecture, where the heterogeneity is introduced using accurate and approximate processing cores. We have considered a case study of radio astronomy calibration processing that employs a complex-input iterative LS algorithm. Our proposed methodology exploits the intrinsic error-resilience of the aforesaid algorithm, where initial iterations are processed on approximate modules while the later ones on accurate modules. Our energy-quality experiments have shown up to 24% of energy savings as compared to an accurate (optimized) counterpart for biased designs and up to 29% energy savings when unbiasing is introduced. The proposed LS accelerator design does not increase the number of iterations and provides sufficient precision to converge to an acceptable solution
Eigenvalues and eigenfunctions of the Laplacian via inverse iteration with shift
In this paper we present an iterative method, inspired by the inverse
iteration with shift technique of finite linear algebra, designed to find the
eigenvalues and eigenfunctions of the Laplacian with homogeneous Dirichlet
boundary condition for arbitrary bounded domains . This
method, which has a direct functional analysis approach, does not approximate
the eigenvalues of the Laplacian as those of a finite linear operator. It is
based on the uniform convergence away from nodal surfaces and can produce a
simple and fast algorithm for computing the eigenvalues with minimal
computational requirements, instead of using the ubiquitous Rayleigh quotient
of finite linear algebra. Also, an alternative expression for the Rayleigh
quotient in the associated infinite dimensional Sobolev space which avoids the
integration of gradients is introduced and shown to be more efficient. The
method can also be used in order to produce the spectral decomposition of any
given function .Comment: In this version the numerical tests in Section 6 were considerably
improved and the Section 5 entitled "Normalization at each step" was
introduced. Moreover, minor adjustments in the Section 1 (Introduction) and
in the Section 7 (Fi nal Comments) were made. Breno Loureiro Giacchini was
added as coautho
Computing better approximate pure Nash equilibria in cut games via semidefinite programming
Cut games are among the most fundamental strategic games in algorithmic game
theory. It is well-known that computing an exact pure Nash equilibrium in these
games is PLS-hard, so research has focused on computing approximate equilibria.
We present a polynomial-time algorithm that computes -approximate pure
Nash equilibria in cut games. This is the first improvement to the previously
best-known bound of , due to the work of Bhalgat, Chakraborty, and Khanna
from EC 2010. Our algorithm is based on a general recipe proposed by
Caragiannis, Fanelli, Gravin, and Skopalik from FOCS 2011 and applied on
several potential games since then. The first novelty of our work is the
introduction of a phase that can identify subsets of players who can
simultaneously improve their utilities considerably. This is done via
semidefinite programming and randomized rounding. In particular, a negative
objective value to the semidefinite program guarantees that no such
considerable improvement is possible for a given set of players. Otherwise,
randomized rounding of the SDP solution is used to identify a set of players
who can simultaneously improve their strategies considerably and allows the
algorithm to make progress. The way rounding is performed is another important
novelty of our work. Here, we exploit an idea that dates back to a paper by
Feige and Goemans from 1995, but we take it to an extreme that has not been
analyzed before
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