5,609 research outputs found
Generic Connectivity-Based CGRA Mapping via Integer Linear Programming
Coarse-grained reconfigurable architectures (CGRAs) are programmable logic
devices with large coarse-grained ALU-like logic blocks, and multi-bit
datapath-style routing. CGRAs often have relatively restricted data routing
networks, so they attract CAD mapping tools that use exact methods, such as
Integer Linear Programming (ILP). However, tools that target general
architectures must use large constraint systems to fully describe an
architecture's flexibility, resulting in lengthy run-times. In this paper, we
propose to derive connectivity information from an otherwise generic device
model, and use this to create simpler ILPs, which we combine in an iterative
schedule and retain most of the exactness of a fully-generic ILP approach. This
new approach has a speed-up geometric mean of 5.88x when considering benchmarks
that do not hit a time-limit of 7.5 hours on the fully-generic ILP, and 37.6x
otherwise. This was measured using the set of benchmarks used to originally
evaluate the fully-generic approach and several more benchmarks representing
computation tasks, over three different CGRA architectures. All run-times of
the new approach are less than 20 minutes, with 90th percentile time of 410
seconds. The proposed mapping techniques are integrated into, and evaluated
using the open-source CGRA-ME architecture modelling and exploration framework.Comment: 8 pages of content; 8 figures; 3 tables; to appear in FCCM 2019; Uses
the CGRA-ME framework at http://cgra-me.ece.utoronto.ca
Clustered Integer 3SUM via Additive Combinatorics
We present a collection of new results on problems related to 3SUM,
including:
1. The first truly subquadratic algorithm for
1a. computing the (min,+) convolution for monotone increasing
sequences with integer values bounded by ,
1b. solving 3SUM for monotone sets in 2D with integer coordinates
bounded by , and
1c. preprocessing a binary string for histogram indexing (also
called jumbled indexing).
The running time is:
with
randomization, or deterministically. This greatly improves the
previous time bound obtained from Williams'
recent result on all-pairs shortest paths [STOC'14], and answers an open
question raised by several researchers studying the histogram indexing problem.
2. The first algorithm for histogram indexing for any constant alphabet size
that achieves truly subquadratic preprocessing time and truly sublinear query
time.
3. A truly subquadratic algorithm for integer 3SUM in the case when the given
set can be partitioned into clusters each covered by an interval
of length , for any constant .
4. An algorithm to preprocess any set of integers so that subsequently
3SUM on any given subset can be solved in
time.
All these results are obtained by a surprising new technique, based on the
Balog--Szemer\'edi--Gowers Theorem from additive combinatorics
A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation
Intrinsic isometric shape matching has become the standard approach for pose
invariant correspondence estimation among deformable shapes. Most existing
approaches assume global consistency, i.e., the metric structure of the whole
manifold must not change significantly. While global isometric matching is well
understood, only a few heuristic solutions are known for partial matching.
Partial matching is particularly important for robustness to topological noise
(incomplete data and contacts), which is a common problem in real-world 3D
scanner data. In this paper, we introduce a new approach to partial, intrinsic
isometric matching. Our method is based on the observation that isometries are
fully determined by purely local information: a map of a single point and its
tangent space fixes an isometry for both global and the partial maps. From this
idea, we develop a new representation for partial isometric maps based on
equivalence classes of correspondences between pairs of points and their
tangent spaces. From this, we derive a local propagation algorithm that find
such mappings efficiently. In contrast to previous heuristics based on RANSAC
or expectation maximization, our method is based on a simple and sound
theoretical model and fully deterministic. We apply our approach to register
partial point clouds and compare it to the state-of-the-art methods, where we
obtain significant improvements over global methods for real-world data and
stronger guarantees than previous heuristic partial matching algorithms.Comment: 17 pages, 12 figure
Development of Computer Science Disciplines - A Social Network Analysis Approach
In contrast to many other scientific disciplines, computer science considers
conference publications. Conferences have the advantage of providing fast
publication of papers and of bringing researchers together to present and
discuss the paper with peers. Previous work on knowledge mapping focused on the
map of all sciences or a particular domain based on ISI published JCR (Journal
Citation Report). Although this data covers most of important journals, it
lacks computer science conference and workshop proceedings. That results in an
imprecise and incomplete analysis of the computer science knowledge. This paper
presents an analysis on the computer science knowledge network constructed from
all types of publications, aiming at providing a complete view of computer
science research. Based on the combination of two important digital libraries
(DBLP and CiteSeerX), we study the knowledge network created at
journal/conference level using citation linkage, to identify the development of
sub-disciplines. We investigate the collaborative and citation behavior of
journals/conferences by analyzing the properties of their co-authorship and
citation subgraphs. The paper draws several important conclusions. First,
conferences constitute social structures that shape the computer science
knowledge. Second, computer science is becoming more interdisciplinary. Third,
experts are the key success factor for sustainability of journals/conferences
Batch kernel SOM and related Laplacian methods for social network analysis
Large graphs are natural mathematical models for describing the structure of
the data in a wide variety of fields, such as web mining, social networks,
information retrieval, biological networks, etc. For all these applications,
automatic tools are required to get a synthetic view of the graph and to reach
a good understanding of the underlying problem. In particular, discovering
groups of tightly connected vertices and understanding the relations between
those groups is very important in practice. This paper shows how a kernel
version of the batch Self Organizing Map can be used to achieve these goals via
kernels derived from the Laplacian matrix of the graph, especially when it is
used in conjunction with more classical methods based on the spectral analysis
of the graph. The proposed method is used to explore the structure of a
medieval social network modeled through a weighted graph that has been directly
built from a large corpus of agrarian contracts
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