Enhancing Speedup in Network Processing Applications by Exploiting Instruction Reuse with Flow Aggregation

Instruction reuse is a microarchitectural technique that improves the execution time of a program by removing redundant computations at run-time. Although this is the job of an optimizing compiler, they do not succeed many a time due to limited knowledge of run-time data. In this paper we examine instruction reuse of integer ALU and load instructions in network processing applications. Specifically, this paper attempts to answer the following questions: (1) How much of instruction reuse is inherent in network processing applications?, (2) Can reuse be improved by reducing interference in the reuse buffer?, (3) What characteristics of network applications can be exploited to improve reuse?, and (4) What is the effect of reuse on resource contention and memory accesses? We propose an aggregation scheme that combines the high-level concept of network traffic i.e. "flows" with a low level microarchitectural feature of programs i.e. repetition of instructions and data along with an architecture that exploits temporal locality in incoming packet data to improve reuse. We find that for the benchmarks considered, 1% to 50% of instructions are reused while the speedup achieved varies between 1% and 24%. As a side effect, instruction reuse reduces memory traffic and can therefore be considered as a scheme for low power.

Energy-efficient synthesis of ferrite powders and films

In recent years, there has been significant effort in the synthesis of nanocrystalline spinel ferrites due to their unique properties. Among them, zinc ferrite has been widely investigated for countless applications. As traditional ferrite synthesis methods are energy- and time-intensive, there is need for a resource-effective process that can prepare ferrites quickly and efficiently without compromising material quality. We report on a novel microwave-assisted soft-chemical synthesis technique in the liquid medium for synthesis of ZnFe2O4 powder below 100 °C, within 5 min. The use of β-diketonate precursors, featuring direct metal-to-oxygen bonds in their molecular structure, not only reduces process temperature and duration sharply, but also leads to water-soluble and non-toxic by-products. As synthesized powder is annealed at 300 °C for 2 hrs in a conventional anneal (CA) schedule. An alternative procedure, a 2-min rapid anneal at 300 °C (RA) is shown to be sufficient to crystallize the ferrite particles, which show a saturation magnetization (MS) of 38 emu/g, compared with 39 emu/g for a 2-hr CA. This signifies that our process is efficient enough to reduce energy consumption by ∼85% just by altering the anneal scheme. Recognizing the criticality of anneal process to the energy budget, a more energy-efficient variation of the reaction process was developed, which obviates the need for post-synthesis annealing altogether. It is shown that the process also can be employed to deposit crystalline thin films of ferrites.

Scalable Preference Aggregation in Social Networks

In social choice theory, preference aggregation refers to computing an aggregate preference over a set of alternatives given individual preferences of all the agents. In real-world scenarios, it may not be feasible to gather preferences from all the agents. Moreover, determining the aggregate preference is computationally intensive. In this paper, we show that the aggregate preference of the agents in a social network can be computed efficiently and with sufficient accuracy using preferences elicited from a small subset of critical nodes in the network. Our methodology uses a model developed based on real-world data obtained using a survey on human subjects, and exploits network structure and homophily of relationships. Our approach guarantees good performance for aggregation rules that satisfy a property which we call expected weak insensitivity. We demonstrate empirically that many practically relevant aggregation rules satisfy this property. We also show that two natural objective functions in this context satisfy certain properties, which makes our methodology attractive for scalable preference aggregation over large scale social networks. We conclude that our approach is superior to random polling while aggregating preferences related to individualistic metrics, whereas random polling is acceptable in the case of social metrics.

An 8-to-1 bit 1-MS/s SAR ADC With VGA and Integrated Data Compression for Neural Recording

Low power consumption per channel and data rate minimization are two key challenges which need to be addressed in future generations of neural recording systems (NRS). Power consumption can be reduced by avoiding unnecessary processing whereas data rate is greatly decreased by sending spike time-stamps along with spike features as opposed to raw digitized data. Dynamic range in NRS can vary with time due to change in electrode-neuron distance or background noise, which demands adaptability. An analog-to-digital converter (ADC) is one of the most important blocks in a NRS. This paper presents an 8-bit SAR ADC in 0.13-mu m CMOS technology along with input and reference buffer. A novel energy efficient digital-to-analog converter switching scheme is proposed, which consumes 37% less energy than the present state-of-the-art. The use of a ping-pong input sampling scheme is emphasized for multichannel input to alleviate the bandwidth requirement of the input buffer. To reduce the data rate, the A/D process is only enabled through the in-built background noise rejection logic to ensure that the noise is not processed. The ADC resolution can be adjusted from 8 to 1 bit in 1-bit step based on the input dynamic range. The ADC consumes 8.8 mu W from 1 V supply at 1 MS/s speed. It achieves effective number of bits of 7.7 bits and FoM of 42.3 fJ/conversion-step.

Identifying services from Legacy batch applications

Transaction processing is a key constituent of the IT workload of commercial enterprises (e.g., banks, insurance companies). Even today, in many large enterprises, transaction processing is done by legacy "batch" applications, which run offline and process accumulated transactions. Developers acknowledge the presence of multiple loosely coupled pieces of functionality within individual applications. Identifying such pieces of functionality (which we call "services") is desirable for the maintenance and evolution of these legacy applications. This is a hard problem, which enterprises grapple with, and one without satisfactory automated solutions. In this paper, we propose a novel static-analysis-based solution to the problem of identifying services within transaction-processing programs. We provide a formal characterization of services in terms of control-flow and data-flow properties, which is well-suited to the idioms commonly exhibited by business applications. Our technique combines program slicing with the detection of conditional code regions to identify services in accordance with our characterization. A preliminary evaluation, based on a manual analysis of three real business programs, indicates that our approach can be effective in identifying useful services from batch applications.

Learning from positive and unlabelled examples using maximum margin clustering

Learning from Positive and Unlabelled examples (LPU) has emerged as an important problem in data mining and information retrieval applications. Existing techniques are not ideally suited for real world scenarios where the datasets are linearly inseparable, as they either build linear classifiers or the non-linear classifiers fail to achieve the desired performance. In this work, we propose to extend maximum margin clustering ideas and present an iterative procedure to design a non-linear classifier for LPU. In particular, we build a least squares support vector classifier, suitable for handling this problem due to symmetry of its loss function. Further, we present techniques for appropriately initializing the labels of unlabelled examples and for enforcing the ratio of positive to negative examples while obtaining these labels. Experiments on real-world datasets demonstrate that the non-linear classifier designed using the proposed approach gives significantly better generalization performance than the existing relevant approaches for LPU.

Fabrication of a novel biomaterial with enhanced mechanical and conducting properties

Conducting polymers have the combined advantages of metal conductivity with ease in processing and biocompatibility; making them extremely versatile for biosensor and tissue engineering applications. However, the inherent brittle property of conducting polymers limits their direct use in such applications which generally warrant soft and flexible material responses. Addition of fillers increases the material compliance, but is achieved at the cost of reduced electrical conductivity. To retain suitable conductivity without compromising the mechanical properties, we fabricate an electroactive blend (dPEDOT) using low grade PEDOT: PSS as the base conducting polymer with polyvinyl alcohol as filler and glycerol as a dopant. Bulk dPEDOT films show a thermally stable response till 110 degrees C with over seven fold increase in room temperature conductivity as compared to 0.002 S cm(-1) for pristine PEDOT: PSS. We characterize the nonlinear stress-strain response of dPEDOT, well described using a Mooney-Rivlin hyperelastic model, and report elastomer-like moduli with ductility similar to fives times its original length. Dynamic mechanical analysis shows constant storage moduli over a large range of frequencies with corresponding linear increase in tan(delta). We relate the enhanced performance of dPEDOT with the underlying structural constituents using FTIR and AFM microscopy. These data demonstrate specific interactions between individual components of dPEDOT, and their effect on surface topography and material properties. Finally, we show biocompatibility of dPEDOT using fibroblasts that have comparable cell morphologies and viability as the control, which make dPEDOT attractive as a biomaterial.

Race Detection for Android Applications

Programming environments for smartphones expose a concurrency model that combines multi-threading and asynchronous event-based dispatch. While this enables the development of efficient and feature-rich applications, unforeseen thread interleavings coupled with non-deterministic reorderings of asynchronous tasks can lead to subtle concurrency errors in the applications. In this paper, we formalize the concurrency semantics of the Android programming model. We further define the happens-before relation for Android applications, and develop a dynamic race detection technique based on this relation. Our relation generalizes the so far independently studied happens-before relations for multi-threaded programs and single-threaded event-driven programs. Additionally, our race detection technique uses a model of the Android runtime environment to reduce false positives. We have implemented a tool called DROIDRACER. It generates execution traces by systematically testing Android applications and detects data races by computing the happens-before relation on the traces. We analyzed 1 5 Android applications including popular applications such as Facebook, Twitter and K-9 Mail. Our results indicate that data races are prevalent in Android applications, and that DROIDRACER is an effective tool to identify data races.

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

Multifarious facets of sugar-derived molecular gels: molecular features, mechanisms of self-assembly and emerging applications

The remarkable capability of nature to design and create excellent self-assembled nano-structures, especially in the biological world, has motivated chemists to mimic such systems with synthetic molecular and supramolecular systems. The hierarchically organized self-assembly of low molecular weight gelators (LMWGs) based on non-covalent interactions has been proven to be a useful tool in the development of well-defined nanostructures. Among these, the self-assembly of sugar-derived LMWGs has received immense attention because of their propensity to furnish biocompatible, hierarchical, supramolecular architectures that are macroscopically expressed in gel formation. This review sheds light on various aspects of sugar-derived LMWGs, uncovering their mechanisms of gelation, structural analysis, and tailorable properties, and their diverse applications such as stimuli-responsiveness, sensing, self-healing, environmental problems, and nano and biomaterials synthesis.

Energy Harvesting WSNs for Accurately Estimating the Maximum Sensor Reading: Trade-Offs and Optimal Design

Computing the maximum of sensor readings arises in several environmental, health, and industrial monitoring applications of wireless sensor networks (WSNs). We characterize the several novel design trade-offs that arise when green energy harvesting (EH) WSNs, which promise perpetual lifetimes, are deployed for this purpose. The nodes harvest renewable energy from the environment for communicating their readings to a fusion node, which then periodically estimates the maximum. For a randomized transmission schedule in which a pre-specified number of randomly selected nodes transmit in a sensor data collection round, we analyze the mean absolute error (MAE), which is defined as the mean of the absolute difference between the maximum and that estimated by the fusion node in each round. We optimize the transmit power and the number of scheduled nodes to minimize the MAE, both when the nodes have channel state information (CSI) and when they do not. Our results highlight how the optimal system operation depends on the EH rate, availability and cost of acquiring CSI, quantization, and size of the scheduled subset. Our analysis applies to a general class of sensor reading and EH random processes.

A Fast and Fault-Tolerant Distributed Algorithm for Near-Optimal TDMA Scheduling in WSNs

The time division multiple access (TDMA) based channel access mechanisms perform better than the contention based channel access mechanisms, in terms of channel utilization, reliability and power consumption, specially for high data rate applications in wireless sensor networks (WSNs). Most of the existing distributed TDMA scheduling techniques can be classified as either static or dynamic. The primary purpose of static TDMA scheduling algorithms is to improve the channel utilization by generating a schedule of smaller length. But, they usually take longer time to schedule, and hence, are not suitable for WSNs, in which the network topology changes dynamically. On the other hand, dynamic TDMA scheduling algorithms generate a schedule quickly, but they are not efficient in terms of generated schedule length. In this paper, we propose a novel scheme for TDMA scheduling in WSNs, which can generate a compact schedule similar to static scheduling algorithms, while its runtime performance can be matched with those of dynamic scheduling algorithms. Furthermore, the proposed distributed TDMA scheduling algorithm has the capability to trade-off schedule length with the time required to generate the schedule. This would allow the developers of WSNs, to tune the performance, as per the requirement of prevalent WSN applications, and the requirement to perform re-scheduling. Finally, the proposed TDMA scheduling is fault-tolerant to packet loss due to erroneous wireless channel. The algorithm has been simulated using the Castalia simulator to compare its performance with those of others in terms of generated schedule length and the time required to generate the TDMA schedule. Simulation results show that the proposed algorithm generates a compact schedule in a very less time.

Synthesis of Eu3+-activated BaMoO4 phosphors and their Judd-Ofelt analysis: Applications in lasers and white LEDs

Eu3+-activated BaMoO4 phosphors were synthesized by the nitrate citrate gel combustion method. The Rietveld refinement analysis confirmed that all the compounds were crystallized in the scheelite-type tetragonal structure with I4(1)/a (No. 88) space group. Photoluminescence (PL) spectra of BaMoO4 phosphor reveals broad emission peaks at 465 and 605 nm, whereas the Eu3+-activated BaMoO4 phosphors show intense 615 nm (D-5(0) -> F-7(2)) emission peak. Judd-Ofelt theory was applied to evaluate the intensity parameters (Omega(2), Omega(4)) of Eu3+-activated BaMoO4 phosphors. The transition probabilities (A(T)), radiative lifetime (tau(rad)), branching ratio (beta), stimulated emission cross-section (sigma(e)), gain bandwidth (sigma(e) x Delta lambda(eff)) and optical gain (sigma(e) x tau(rad)) were investigated by using the intensity parameters. CIE color coordinates confirmed that the BaMoO4 and Eu3+-activated BaMoO4 phosphors exhibit white and red luminescence, respectively. The obtained results revealed that the present phosphors can be a potential candidate for red lasers and white LEDs applications. (C) 2015 Elsevier B.V. All rights reserved.

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.

Molecular structure of the discotic liquid crystalline phase of hexa-peri-hexabenzocoronene/oligothiophene hybrid and their charge transport properties

Using atomistic molecular dynamics simulation, we study the discotic columnar liquid crystalline (LC) phases formed by a new organic compound having hexa-peri-Hexabenzocoronene (HBC) core with six pendant oligothiophene units recently synthesized by Nan Hu et al. Adv. Mater. 26, 2066 (2014)]. This HBC core based LC phase was shown to have electric field responsive behavior and has important applications in organic electronics. Our simulation results confirm the hexagonal arrangement of columnar LC phase with a lattice spacing consistent with that obtained from small angle X-ray diffraction data. We have also calculated various positional and orientational correlation functions to characterize the ordering of the molecules in the columnar arrangement. The molecules in a column are arranged with an average twist of 25 degrees having an average inter-molecular separation of similar to 5 angstrom. Interestingly, we find an overall tilt angle of 43 degrees between the columnar axis and HBC core. We also simulate the charge transport through this columnar phase and report the numerical value of charge carrier mobility for this liquid crystal phase. The charge carrier mobility is strongly influenced by the twist angle and average spacing of the molecules in the column. (C) 2015 AIP Publishing LLC.