Traffic engineered NoC for streaming applications

Streaming applications demand hard bandwidth and throughput guarantees in a multiprocessor environment amidst resource competing processes. We present a Label Switching based Network-on-Chip (LS-NoC) motivated by throughput guarantees offered by bandwidth reservation. Label switching is a packet relaying technique in which individual packets carry route information in the form of labels. A centralized LS-NoC Management framework engineers traffic into Quality of Service (QoS) guaranteed routes. LS-NoC caters to the requirements of streaming applications where communication channels are fixed over the lifetime of the application. The proposed NoC framework inherently supports heterogeneous and ad hoc system-on-chips. The LS-NoC can be used in conjunction with conventional best effort NoC as a QoS guaranteed communication network or as a replacement to the conventional NoC. A multicast, broadcast capable label switched router for the LS-NoC has been designed. A 5 port, 256 bit data bus, 4 bit label router occupies 0.431 mm(2) in 130 nm and delivers peak bandwidth of 80 Gbits/s per link at 312.5 MHz. Bandwidth and latency guarantees of LS-NoC have been demonstrated on traffic from example streaming applications and on constant and variable bit rate traffic patterns. LS-NoC was found to have a competitive AreaxPower/Throughput figure of merit with state-of-the-art NoCs providing QoS. Circuit switching with link sharing abilities and support for asynchronous operation make LS-NoC a desirable choice for QoS servicing in chip multiprocessors. (C) 2013 Elsevier B.V. All rights reserved.

EXTENSIONS OF VECTOR BUNDLES WITH APPLICATION TO NOETHER-LEFSCHETZ THEOREMS

Given a smooth, projective variety Y over an algebraically closed field of characteristic zero, and a smooth, ample hyperplane section X subset of Y, we study the question of when a bundle E on X, extends to a bundle epsilon on a Zariski open set U subset of Y containing X. The main ingredients used are explicit descriptions of various obstruction classes in the deformation theory of bundles, together with Grothendieck-Lefschetz theory. As a consequence, we prove a Noether-Lefschetz theorem for higher rank bundles, which recovers and unifies the Noether-Lefschetz theorems of Joshi and Ravindra-Srinivas.

Fractional Hilbert transform extensions and associated analytic signal construction

The analytic signal (AS) was proposed by Gabor as a complex signal corresponding to a given real signal. The AS has a one-sided spectrum and gives rise to meaningful spectral averages. The Hilbert transform (HT) is a key component in Gabor's AS construction. We generalize the construction methodology by employing the fractional Hilbert transform (FrHT), without going through the standard fractional Fourier transform (FrFT) route. We discuss some properties of the fractional Hilbert operator and show how decomposition of the operator in terms of the identity and the standard Hilbert operators enables the construction of a family of analytic signals. We show that these analytic signals also satisfy Bedrosian-type properties and that their time-frequency localization properties are unaltered. We also propose a generalized-phase AS (GPAS) using a generalized-phase Hilbert transform (GPHT). We show that the GPHT shares many properties of the FrHT, in particular, selective highlighting of singularities, and a connection with Lie groups. We also investigate the duality between analyticity and causality concepts to arrive at a representation of causal signals in terms of the FrHT and GPHT. On the application front, we develop a secure multi-key single-sideband (SSB) modulation scheme and analyze its performance in noise and sensitivity to security key perturbations. (C) 2013 Elsevier B.V. All rights reserved.

USEFUL EXTENSIONS OF THE HENRY REACTION: EXPEDITIOUS ROUTES TO NITROALKANES AND NITROALKENES IN AQUEOUS MEDIA

The products of the Henry nitroaldol reaction from nitromethane and several aldehydes were reduced to the corresponding nitroalkanes with (n-Bu)(3)SnH in water under microwave irradiation (80 degrees C/10 min), or dehydrated to the corresponding nitroalkenes with K2CO3 in water (generally 0-5 degrees C/20 min). Both ``one-pot'' reactions occur in excellent yields across a range of aliphatic and aromatic (including heteroaromatic) substrates. It seems likely that the deoxygenation of the nitroaldols occurs via coordination of an oxygen atom of the nitro group with a tin atom, which facilitates hydride delivery in the transition state. The elimination of water from the nitroaldols in mild base is likely driven by the stability of the conjugated nitroalkene products. The elimination required workup with 2N HCl, which likely displaces a nitroalkane-nitroalkene equilibrium towards the latter. These extensions of the Henry reaction lead to products not easily obtained otherwise.

Survey of vector-like fermion extensions of the Standard Model and their phenomenological implications

With the renewed interest in vector-like fermion extensions of the Standard Model, we present here a study of multiple vector-like theories and their phenomenological implications. Our focus is mostly on minimal flavor conserving theories that couple the vector-like fermions to the SM gauge fields and mix only weakly with SM fermions so as to avoid flavor problems. We present calculations for precision electroweak and vector-like state decays, which are needed to investigate compatibility with currently known data. We investigate the impact of vector-like fermions on Higgs boson production and decay, including loop contributions, in a wide variety of vector-like extensions and their parameter spaces.

Synthesis of Instruction Extensions on HyperCell, a Reconfigurable Datapath

In this paper we present HyperCell as a reconfigurable datapath for Instruction Extensions (IEs). HyperCell comprises an array of compute units laid over a switch network. We present an IE synthesis methodology that enables post-silicon realization of IE datapaths on HyperCell. The synthesis methodology optimally exploits hardware resources in HyperCell to enable software pipelined execution of IEs. Exploitation of temporal reuse of data in HyperCell results in significant reduction of input/output bandwidth requirements of HyperCell.

Spectral Clustering with Jensen-type kernels and their multi-point extensions

Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multipoint' kernels, and study their applications. We study a class of kernels based on Jensen type divergences and show that these can be extended to measure similarity among multiple points. We study tensor flattening methods and develop a multi-point (kernel) spectral clustering (MSC) method. We further emphasize on a special case of the proposed kernels, which is a multi-point extension of the linear (dot-product) kernel and show the existence of cubic time tensor flattening algorithm in this case. Finally, we illustrate the usefulness of our contributions using standard data sets and image segmentation tasks.

Prototype Selection on Large and Streaming Data

Since streaming data keeps coming continuously as an ordered sequence, massive amounts of data is created. A big challenge in handling data streams is the limitation of time and space. Prototype selection on streaming data requires the prototypes to be updated in an incremental manner as new data comes in. We propose an incremental algorithm for prototype selection. This algorithm can also be used to handle very large datasets. Results have been presented on a number of large datasets and our method is compared to an existing algorithm for streaming data. Our algorithm saves time and the prototypes selected gives good classification accuracy.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.