12,583 research outputs found
A Multi-objective Perspective for Operator Scheduling using Fine-grained DVS Architecture
The stringent power budget of fine grained power managed digital integrated
circuits have driven chip designers to optimize power at the cost of area and
delay, which were the traditional cost criteria for circuit optimization. The
emerging scenario motivates us to revisit the classical operator scheduling
problem under the availability of DVFS enabled functional units that can
trade-off cycles with power. We study the design space defined due to this
trade-off and present a branch-and-bound(B/B) algorithm to explore this state
space and report the pareto-optimal front with respect to area and power. The
scheduling also aims at maximum resource sharing and is able to attain
sufficient area and power gains for complex benchmarks when timing constraints
are relaxed by sufficient amount. Experimental results show that the algorithm
that operates without any user constraint(area/power) is able to solve the
problem for most available benchmarks, and the use of power budget or area
budget constraints leads to significant performance gain.Comment: 18 pages, 6 figures, International journal of VLSI design &
Communication Systems (VLSICS
Compressive Mining: Fast and Optimal Data Mining in the Compressed Domain
Real-world data typically contain repeated and periodic patterns. This
suggests that they can be effectively represented and compressed using only a
few coefficients of an appropriate basis (e.g., Fourier, Wavelets, etc.).
However, distance estimation when the data are represented using different sets
of coefficients is still a largely unexplored area. This work studies the
optimization problems related to obtaining the \emph{tightest} lower/upper
bound on Euclidean distances when each data object is potentially compressed
using a different set of orthonormal coefficients. Our technique leads to
tighter distance estimates, which translates into more accurate search,
learning and mining operations \textit{directly} in the compressed domain.
We formulate the problem of estimating lower/upper distance bounds as an
optimization problem. We establish the properties of optimal solutions, and
leverage the theoretical analysis to develop a fast algorithm to obtain an
\emph{exact} solution to the problem. The suggested solution provides the
tightest estimation of the -norm or the correlation. We show that typical
data-analysis operations, such as k-NN search or k-Means clustering, can
operate more accurately using the proposed compression and distance
reconstruction technique. We compare it with many other prevalent compression
and reconstruction techniques, including random projections and PCA-based
techniques. We highlight a surprising result, namely that when the data are
highly sparse in some basis, our technique may even outperform PCA-based
compression.
The contributions of this work are generic as our methodology is applicable
to any sequential or high-dimensional data as well as to any orthogonal data
transformation used for the underlying data compression scheme.Comment: 25 pages, 20 figures, accepted in VLD
Data dependent energy modelling for worst case energy consumption analysis
Safely meeting Worst Case Energy Consumption (WCEC) criteria requires
accurate energy modeling of software. We investigate the impact of instruction
operand values upon energy consumption in cacheless embedded processors.
Existing instruction-level energy models typically use measurements from random
input data, providing estimates unsuitable for safe WCEC analysis.
We examine probabilistic energy distributions of instructions and propose a
model for composing instruction sequences using distributions, enabling WCEC
analysis on program basic blocks. The worst case is predicted with statistical
analysis. Further, we verify that the energy of embedded benchmarks can be
characterised as a distribution, and compare our proposed technique with other
methods of estimating energy consumption
Pruned Continuous Haar Transform of 2D Polygonal Patterns with Application to VLSI Layouts
We introduce an algorithm for the efficient computation of the continuous
Haar transform of 2D patterns that can be described by polygons. These patterns
are ubiquitous in VLSI processes where they are used to describe design and
mask layouts. There, speed is of paramount importance due to the magnitude of
the problems to be solved and hence very fast algorithms are needed. We show
that by techniques borrowed from computational geometry we are not only able to
compute the continuous Haar transform directly, but also to do it quickly. This
is achieved by massively pruning the transform tree and thus dramatically
decreasing the computational load when the number of vertices is small, as is
the case for VLSI layouts. We call this new algorithm the pruned continuous
Haar transform. We implement this algorithm and show that for patterns found in
VLSI layouts the proposed algorithm was in the worst case as fast as its
discrete counterpart and up to 12 times faster.Comment: 4 pages, 5 figures, 1 algorith
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Survey of partitioning techniques in silicon compilation
In the silicon compilation design process, partitioning is usually the first problem to be investigated because partitioning algorithms form the backbone of many algorithms including: system synthesis, processor synthesis, floorplanning, and placement. In this survey, several partitioning techniques will be examined. In addition, this paper will review the partitioning algorithms used by synthesis systems at different design levels
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