420 research outputs found
Open Problems in (Hyper)Graph Decomposition
Large networks are useful in a wide range of applications. Sometimes problem
instances are composed of billions of entities. Decomposing and analyzing these
structures helps us gain new insights about our surroundings. Even if the final
application concerns a different problem (such as traversal, finding paths,
trees, and flows), decomposing large graphs is often an important subproblem
for complexity reduction or parallelization. This report is a summary of
discussions that happened at Dagstuhl seminar 23331 on "Recent Trends in Graph
Decomposition" and presents currently open problems and future directions in
the area of (hyper)graph decomposition
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
In the past decade, Convolutional Neural Networks (CNNs) have demonstrated
state-of-the-art performance in various Artificial Intelligence tasks. To
accelerate the experimentation and development of CNNs, several software
frameworks have been released, primarily targeting power-hungry CPUs and GPUs.
In this context, reconfigurable hardware in the form of FPGAs constitutes a
potential alternative platform that can be integrated in the existing deep
learning ecosystem to provide a tunable balance between performance, power
consumption and programmability. In this paper, a survey of the existing
CNN-to-FPGA toolflows is presented, comprising a comparative study of their key
characteristics which include the supported applications, architectural
choices, design space exploration methods and achieved performance. Moreover,
major challenges and objectives introduced by the latest trends in CNN
algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and
in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal,
201
Efficient Approximation Algorithms for Multi-Antennae Largest Weight Data Retrieval
In a mobile network, wireless data broadcast over channels (frequencies)
is a powerful means for distributed dissemination of data to clients who access
the channels through multi-antennae equipped on their mobile devices. The
-antennae largest weight data retrieval (ALWDR) problem is to
compute a schedule for downloading a subset of data items that has a maximum
total weight using antennae in a given time interval. In this paper,
we propose a ratio approximation algorithm for the
-antennae largest weight data retrieval (ALWDR) problem that
has the same ratio as the known result but a significantly improved time
complexity of from
when
\cite{lu2014data}. To our knowledge, our algorithm represents the first ratio
approximation solution to ALWDR for the
general case of arbitrary . To achieve this, we first give a ratio
algorithm for the -separated ALWDR
(ALWDR) with runtime , under the assumption
that every data item appears at most once in each segment of
ALWDR, for any input of maximum length on channels in
time slots. Then, we show that we can retain the same ratio for
ALWDR without this assumption at the cost of increased time
complexity to . This result immediately yields an
approximation solution of same ratio and time complexity for ALWDR,
presenting a significant improvement of the known time complexity of ratio
approximation to the problem
A Survey of FPGA Optimization Methods for Data Center Energy Efficiency
This article provides a survey of academic literature about field
programmable gate array (FPGA) and their utilization for energy efficiency
acceleration in data centers. The goal is to critically present the existing
FPGA energy optimization techniques and discuss how they can be applied to such
systems. To do so, the article explores current energy trends and their
projection to the future with particular attention to the requirements set out
by the European Code of Conduct for Data Center Energy Efficiency. The article
then proposes a complete analysis of over ten years of research in energy
optimization techniques, classifying them by purpose, method of application,
and impacts on the sources of consumption. Finally, we conclude with the
challenges and possible innovations we expect for this sector.Comment: Accepted for publication in IEEE Transactions on Sustainable
Computin
Flow Decomposition With Subpath Constraints
Flow network decomposition is a natural model for problems where we are given a flow network arising from superimposing a set of weighted paths and would like to recover the underlying data, i.e., decompose the flow into the original paths and their weights. Thus, variations on flow decomposition are often used as subroutines in multiassembly problems such as RNA transcript assembly. In practice, we frequently have access to information beyond flow values in the form of subpaths, and many tools incorporate these heuristically. But despite acknowledging their utility in practice, previous work has not formally addressed the effect of subpath constraints on the accuracy of flow network decomposition approaches. We formalize the flow decomposition with subpath constraints problem, give the first algorithms for it, and study its usefulness for recovering ground truth decompositions. For finding a minimum decomposition, we propose both a heuristic and an FPTalgorithm. Experiments on RNA transcript datasets show that for instances with larger solution path sets, the addition of subpath constraints finds 13% more ground truth solutions when minimal decompositions are found exactly, and 30% more ground truth solutions when minimal decompositions are found heuristically.Peer reviewe
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