17,607 research outputs found
Unifying mesh- and tree-based programmable interconnect
We examine the traditional, symmetric, Manhattan mesh design for field-programmable gate-array (FPGA) routing along with tree-of-meshes (ToM) and mesh-of-trees (MoT) based designs. All three networks can provide general routing for limited bisection designs (Rent's rule with p<1) and allow locality exploitation. They differ in their detailed topology and use of hierarchy. We show that all three have the same asymptotic wiring requirements. We bound this tightly by providing constructive mappings between routes in one network and routes in another. For example, we show that a (c,p) MoT design can be mapped to a (2c,p) linear population ToM and introduce a corner turn scheme which will make it possible to perform the reverse mapping from any (c,p) linear population ToM to a (2c,p) MoT augmented with a particular set of corner turn switches. One consequence of this latter mapping is a multilayer layout strategy for N-node, linear population ToM designs that requires only /spl Theta/(N) two-dimensional area for any p when given sufficient wiring layers. We further show upper and lower bounds for global mesh routes based on recursive bisection width and show these are within a constant factor of each other and within a constant factor of MoT and ToM layout area. In the process we identify the parameters and characteristics which make the networks different, making it clear there is a unified design continuum in which these networks are simply particular regions
Large Graph Analysis in the GMine System
Current applications have produced graphs on the order of hundreds of
thousands of nodes and millions of edges. To take advantage of such graphs, one
must be able to find patterns, outliers and communities. These tasks are better
performed in an interactive environment, where human expertise can guide the
process. For large graphs, though, there are some challenges: the excessive
processing requirements are prohibitive, and drawing hundred-thousand nodes
results in cluttered images hard to comprehend. To cope with these problems, we
propose an innovative framework suited for any kind of tree-like graph visual
design. GMine integrates (a) a representation for graphs organized as
hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b)
a graph summarization methodology - CEPS. Our graph representation deals with
the problem of tracing the connection aspects of a graph hierarchy with sub
linear complexity, allowing one to grasp the neighborhood of a single node or
of a group of nodes in a single click. As a proof of concept, the visual
environment of GMine is instantiated as a system in which large graphs can be
investigated globally and locally
Design Considerations for Low Power Internet Protocols
Over the past 10 years, low-power wireless networks have transitioned to
supporting IPv6 connectivity through 6LoWPAN, a set of standards which specify
how to aggressively compress IPv6 packets over low-power wireless links such as
802.15.4.
We find that different low-power IPv6 stacks are unable to communicate using
6LoWPAN, and therefore IP, due to design tradeoffs between code size and energy
efficiency. We argue that applying traditional protocol design principles to
low-power networks is responsible for these failures, in part because receivers
must accommodate a wide range of senders.
Based on these findings, we propose three design principles for Internet
protocols on low-power networks. These principles are based around the
importance of providing flexible tradeoffs between code size and energy
efficiency. We apply these principles to 6LoWPAN and show that the resulting
design of the protocol provides developers a wide range of tradeoff points
while allowing implementations with different choices to seamlessly
communicate
Resolving structural variability in network models and the brain
Large-scale white matter pathways crisscrossing the cortex create a complex
pattern of connectivity that underlies human cognitive function. Generative
mechanisms for this architecture have been difficult to identify in part
because little is known about mechanistic drivers of structured networks. Here
we contrast network properties derived from diffusion spectrum imaging data of
the human brain with 13 synthetic network models chosen to probe the roles of
physical network embedding and temporal network growth. We characterize both
the empirical and synthetic networks using familiar diagnostics presented in
statistical form, as scatter plots and distributions, to reveal the full range
of variability of each measure across scales in the network. We focus on the
degree distribution, degree assortativity, hierarchy, topological Rentian
scaling, and topological fractal scaling---in addition to several summary
statistics, including the mean clustering coefficient, shortest path length,
and network diameter. The models are investigated in a progressive, branching
sequence, aimed at capturing different elements thought to be important in the
brain, and range from simple random and regular networks, to models that
incorporate specific growth rules and constraints. We find that synthetic
models that constrain the network nodes to be embedded in anatomical brain
regions tend to produce distributions that are similar to those extracted from
the brain. We also find that network models hardcoded to display one network
property do not in general also display a second, suggesting that multiple
neurobiological mechanisms might be at play in the development of human brain
network architecture. Together, the network models that we develop and employ
provide a potentially useful starting point for the statistical inference of
brain network structure from neuroimaging data.Comment: 24 pages, 11 figures, 1 table, supplementary material
Potential implementation of Reservoir Computing models based on magnetic skyrmions
Reservoir Computing is a type of recursive neural network commonly used for
recognizing and predicting spatio-temporal events relying on a complex
hierarchy of nested feedback loops to generate a memory functionality. The
Reservoir Computing paradigm does not require any knowledge of the reservoir
topology or node weights for training purposes and can therefore utilize
naturally existing networks formed by a wide variety of physical processes.
Most efforts prior to this have focused on utilizing memristor techniques to
implement recursive neural networks. This paper examines the potential of
skyrmion fabrics formed in magnets with broken inversion symmetry that may
provide an attractive physical instantiation for Reservoir Computing.Comment: 11 pages, 3 figure
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