Evaluating Asymmetric Multicore Systems-on-Chip using Iso-Metrics

The end of Dennard scaling has pushed power consumption into a first order concern for current systems, on par with performance. As a result, near-threshold voltage computing (NTVC) has been proposed as a potential means to tackle the limited cooling capacity of CMOS technology. Hardware operating in NTV consumes significantly less power, at the cost of lower frequency, and thus reduced performance, as well as increased error rates. In this paper, we investigate if a low-power systems-on-chip, consisting of ARM's asymmetric big.LITTLE technology, can be an alternative to conventional high performance multicore processors in terms of power/energy in an unreliable scenario. For our study, we use the Conjugate Gradient solver, an algorithm representative of the computations performed by a large range of scientific and engineering codes.

A floating point CORDIC based SVD processor

An SVD processor system is presented in which each processing element is implemented using a simple CORDIC unit. The internal recursive loop within the CORDIC module is exploited, with pipelining being used to multiplex the two independent micro-rotations onto a single CORDIC processor. This leads to a high performance and efficient hardware architecture. In addition, a novel method for scale factor correction is presented which only need be applied once at the end of the computation. This also reduces the computation time. The net result is an SVD architecture based on a conventional CORDIC approach, which combines high performance with high silicon area efficiency.

High Strength Thermoplastic Bonding for Multi-channel, Multi-layer Lab-on-Chip Devices for Ocean and Environmental applications

A solvent-vapour thermoplastic bonding process is reported which provides high strength bonding of PMMA over a large area for multi-channel and multi-layer microfluidic devices with shallow high resolution channel features. The bond process utilises a low temperature vacuum thermal fusion step with prior exposure of the substrate to chloroform (CHCl3) vapour to reduce bond temperature to below the PMMA glass transition temperature. Peak tensile and shear bond strengths greater than 3 MPa were achieved for a typical channel depth reduction of 25 µm. The device-equivalent bond performance was evaluated for multiple layers and high resolution channel features using double-side and single-side exposure of the bonding pieces. A single-sided exposure process was achieved which is suited to multi-layer bonding with channel alignment at the expense of greater depth loss and a reduction in peak bond strength. However, leak and burst tests demonstrate bond integrity up to at least 10 bar channel pressure over the full substrate area of 100 mm x 100 mm. The inclusion of metal tracks within the bond resulted in no loss of performance. The vertical wall integrity between channels was found to be compromised by solvent permeation for wall thicknesses of 100 µm which has implications for high resolution serpentine structures. Bond strength is reduced considerably for multi-layer patterned substrates where features on each layer are not aligned, despite the presence of an intermediate blank substrate. Overall a high performance bond process has been developed that has the potential to meet the stringent specifications for lab-on-chip deployment in harsh environmental conditions for applications such as deep ocean profiling.

Facile synthesis of nanocrystalline LiFePO4/graphene composite as cathode material for high power lithium ion batteries

We describe a simple strategy, which is based on the idea of space confinement, for the synthesis of carbon coating on LiFePO₄ nanoparticles/graphene nanosheets composites in a water-in-oil emulsion system. The prepared composite displayed high performance as a cathode material for lithium-ion battery, such as high reversible lithium storage capacity (158 mA h g^-1 after 100 cycles), high coulombic efficiency (over 97%), excellent cycling stability and high rate capability (as high as 83 mA h g ^-1 at 60 C). Very significantly, the preparation method employed can be easily adapted and be extended as a general approach to sophisticated compositions and structures for the preparation of highly dispersed nanosized structure on graphene. 

Facile Synthesis of Anatase TiO2 Quantum-Dot/Graphene-Nanosheet Composites with Enhanced Electrochemical Performance for Lithium-Ion Batteries

A facile method to synthesize well-dispersed TiO₂ quantum dots on graphene nanosheets (TiO₂-QDs/GNs) in a water-in-oil (W/O) emulsion system is reported. The TiO₂/graphene composites display high performance as an anode material for lithium-ion batteries (LIBs), such as having high reversible lithium storage capacity, high Coulombic efficiency, excellent cycling stability, and high rate capability. The excellent electrochemical performance and special structure of the composites thus offer a way to prepare novel graphene-based electrode materials for high-energy-density and high-power LIBs. 

Streaming Elements for FPGA Signal and Image Processing Accelerators

Field programmable gate array devices boast abundant resources with which custom accelerator components for signal, image and data processing may be realised; however, realising high performance, low cost accelerators currently demands manual register transfer level design. Software-programmable ’soft’ processors have been proposed as a way to reduce this design burden but they are unable to support performance and cost comparable to custom circuits. This paper proposes a new soft processing approach for FPGA which promises to overcome this barrier. A high performance, fine-grained streaming processor, known as a Streaming Accelerator Element, is proposed which realises accelerators as large scale custom multicore networks. By adopting a streaming execution approach with advanced program control and memory addressing capabilities, typical program inefficiencies can be almost completely eliminated to enable performance and cost which are unprecedented amongst software-programmable solutions. When used to realise accelerators for fast fourier transform, motion estimation, matrix multiplication and sobel edge detection it is shown how the proposed architecture enables real-time performance and with performance and cost comparable with hand-crafted custom circuit accelerators and up to two orders of magnitude beyond existing soft processors.

Soft-core Stream Processor for Sliding Window Applications

Software-programmable `soft' processors have shown tremendous potential for efficient realisation of high performance signal processing operations on Field Programmable Gate Array (FPGA), whilst lowering the design burden by avoiding the need to design fine-grained custom circuit archi-tectures. However, the complex data access patterns, high memory bandwidth and computational requirements of sliding window applications, such as Motion Estimation (ME) and Matrix Multiplication (MM), lead to low performance, inefficient soft processor realisations. This paper resolves this issue, showing how by adding support for block data addressing and accelerators for high performance loop execution, performance and resource efficiency over four times better than current best-in-class metrics can be achieved. In addition, it demonstrates the first recorded real-time soft ME estimation realisation for H.263 systems.

FPGA based Streaming Image Processing Processor (SIPPro) architecture to accelerate data intensive applications

Current data-intensive image processing applications push traditional embedded architectures to their limits. FPGA based hardware acceleration is a potential solution but the programmability gap and time consuming HDL design flow is significant. The proposed research approach to develop “FPGA based programmable hardware acceleration platform” that uses, large number of Streaming Image processing Processors (SIPPro) potentially addresses these issues. SIPPro is pipelined in-order soft-core processor architecture with specific optimisations for image processing applications. Each SIPPro core uses 1 DSP48, 2 Block RAMs and 370 slice-registers, making the processor as compact as possible whilst maintaining flexibility and programmability. It is area efficient, scalable and high performance softcore architecture capable of delivering 530 MIPS per core using Xilinx Zynq SoC (ZC7Z020-3). To evaluate the feasibility of the proposed architecture, a Traffic Sign Recognition (TSR) algorithm has been prototyped on a Zedboard with the color and morphology operations accelerated using multiple SIPPros. Simulation and experimental results demonstrate that the processing platform is able to achieve a speedup of 15 and 33 times for color filtering and morphology operations respectively, with a significant reduced design effort and time.

Computing probable maximum loss in catastrophe reinsurance portfolios on multi-core and many-core architectures

In the reinsurance market, the risks natural catastrophes pose to portfolios of properties must be quantified, so that they can be priced, and insurance offered. The analysis of such risks at a portfolio level requires a simulation of up to 800 000 trials with an average of 1000 catastrophic events per trial. This is sufficient to capture risk for a global multi-peril reinsurance portfolio covering a range of perils including earthquake, hurricane, tornado, hail, severe thunderstorm, wind storm, storm surge and riverine flooding, and wildfire. Such simulations are both computation and data intensive, making the application of high-performance computing techniques desirable.

In this paper, we explore the design and implementation of portfolio risk analysis on both multi-core and many-core computing platforms. Given a portfolio of property catastrophe insurance treaties, key risk measures, such as probable maximum loss, are computed by taking both primary and secondary uncertainties into account. Primary uncertainty is associated with whether or not an event occurs in a simulated year, while secondary uncertainty captures the uncertainty in the level of loss due to the use of simplified physical models and limitations in the available data. A combination of fast lookup structures, multi-threading and careful hand tuning of numerical operations is required to achieve good performance. Experimental results are reported for multi-core processors and systems using NVIDIA graphics processing unit and Intel Phi many-core accelerators.

A many-core co-processor for embedded parallel computing on FPGA

Single processor architectures are unable to provide the required performance of high performance embedded systems. Parallel processing based on general-purpose processors can achieve these performances with a considerable increase of required resources. However, in many cases, simplified optimized parallel cores can be used instead of general-purpose processors achieving better performance at lower resource utilization. In this paper, we propose a configurable many-core architecture to serve as a co-processor for high-performance embedded computing on Field-Programmable Gate Arrays. The architecture consists of an array of configurable simple cores with support for floating-point operations interconnected with a configurable interconnection network. For each core it is possible to configure the size of the internal memory, the supported operations and number of interfacing ports. The architecture was tested in a ZYNQ-7020 FPGA in the execution of several parallel algorithms. The results show that the proposed many-core architecture achieves better performance than that achieved with a parallel generalpurpose processor and that up to 32 floating-point cores can be implemented in a ZYNQ-7020 SoC FPGA.

Parallel Software framework for time-critical many-core systems

The recent technological advancements and market trends are causing an interesting phenomenon towards the convergence of High-Performance Computing (HPC) and Embedded Computing (EC) domains. On one side, new kinds of HPC applications are being required by markets needing huge amounts of information to be processed within a bounded amount of time. On the other side, EC systems are increasingly concerned with providing higher performance in real-time, challenging the performance capabilities of current architectures. The advent of next-generation many-core embedded platforms has the chance of intercepting this converging need for predictable high-performance, allowing HPC and EC applications to be executed on efficient and powerful heterogeneous architectures integrating general-purpose processors with many-core computing fabrics. To this end, it is of paramount importance to develop new techniques for exploiting the massively parallel computation capabilities of such platforms in a predictable way. P-SOCRATES will tackle this important challenge by merging leading research groups from the HPC and EC communities. The time-criticality and parallelisation challenges common to both areas will be addressed by proposing an integrated framework for executing workload-intensive applications with real-time requirements on top of next-generation commercial-off-the-shelf (COTS) platforms based on many-core accelerated architectures. The project will investigate new HPC techniques that fulfil real-time requirements. The main sources of indeterminism will be identified, proposing efficient mapping and scheduling algorithms, along with the associated timing and schedulability analysis, to guarantee the real-time and performance requirements of the applications.

Devenir environnemental des antidépresseurs dans les rejets urbains par chromatographie liquide à haute performance couplée à la spectrométrie de masse en tandem

Les troubles reliés à la dépression, l’épuisement professionnel et l’anxiété sont de plus en plus répandus dans notre société moderne. La consommation croissante d’antidépresseurs dans les différents pays du monde est responsable de la récente détection de résidus à l’état de traces dans les rejets urbains municipaux. Ainsi, ces substances dites « émergentes » qui possèdent une activité pharmacologique destinée à la régulation de certains neurotransmetteurs dans le cerveau suscitent maintenant de nombreuses inquiétudes de la part de la communauté scientifique. L’objectif principal de ce projet de doctorat a été de mieux comprendre le devenir de plusieurs classes d’antidépresseurs présents dans diverses matrices environnementales (i.e. eaux de surfaces, eaux usées, boues de traitement, tissus biologiques) en développant de nouvelles méthodes analytiques fiables capables de les détecter, quantifier et confirmer par chromatographie liquide à haute performance couplée à la spectrométrie de masse en tandem (LC-QqQMS, LC-QqToFMS). Une première étude complétée à la station d’épuration de la ville de Montréal a permis de confirmer la présence de six antidépresseurs et quatre métabolites N-desmethyl dans les affluents (2 - 330 ng L-1). Pour ce traitement primaire (physico-chimique), de faibles taux d’enlèvement (≤ 15%) ont été obtenus. Des concentrations d’antidépresseurs atteignant près de 100 ng L-1 ont également été détectées dans le fleuve St-Laurent à 0.5 km du point de rejet de la station d’épuration. Une seconde étude menée à la même station a permis l’extraction sélective d’antidépresseurs dans trois tissus (i.e. foie, cerveau et filet) de truites mouchetées juvéniles exposées à différentes concentrations d’effluent dilué traité et non-traité à l’ozone. Un certain potentiel de bioaccumulation dans les tissus (0.08-10 ng g-1) a été observé pour les spécimens exposés à l’effluent non-traité (20% v/v) avec distribution majoritaire dans le foie et le cerveau. Une intéressante corrélation a été établie entre les concentrations de trois antidépresseurs dans le cerveau et l’activité d’un biomarqueur d’exposition (i.e. pompe N/K ATPase impliquée dans la régulation de la sérotonine) mesurée à partir de synaptosomes de truites exposées aux effluents. Une investigation de l’efficacité de plusieurs stations d’épuration canadiennes opérant différents types de traitements a permis de constater que les traitements secondaires (biologiques) étaient plus performants que ceux primaires (physico-chimiques) pour enlever les antidépresseurs (taux moyen d’enlèvement : 30%). Les teneurs les plus élevées dans les boues traitées (biosolides) ont été obtenues avec le citalopram (1033 ng g-1), la venlafaxine (833 ng g-1) et l’amitriptyline (78 ng g-1). Des coefficients de sorption expérimentaux (Kd) calculés pour chacun des antidépresseurs ont permis d’estimer une grande sorption des composés sertraline, desméthylsertraline, paroxetine et fluoxetine sur les solides (log Kd > 4). Finalement, un excellent taux d’enlèvement moyen de 88% a été obtenu après ozonation (5 mg L-1) d’un effluent primaire. Toutefois, la caractérisation de nouveaux sous-produits N-oxyde (venlafaxine, desmethylvenlafaxine) par spectrométrie de masse à haute résolution (LC-QqToFMS) dans l’effluent traité à l’ozone a mis en lumière la possibilité de formation de multiples composés polaires de toxicité inconnue.

L’optimisation de la performance des services infirmiers : miser sur des pratiques de gestion utilisées par les organisations hautement performantes

Travail dirigé présenté à la Faculté des Sciences Infirmières en vue de l’obtention du grade de Maître ès Sciences (M. Sc.) en sciences infirmière option administration des sciences infirmières

Assessment of Tandem Mass Spectrometry and High Resolution Mass Spectrometry for the Analysis of Bupivacaine in Plasma

Triple quadrupole mass spectrometers coupled with high performance liquid chromatography are workhorses in quantitative bioanalyses. It provides substantial benefits including reproducibility, sensitivity and selectivity for trace analysis. Selected Reaction Monitoring allows targeted assay development but data sets generated contain very limited information. Data mining and analysis of non-targeted high-resolution mass spectrometry profiles of biological samples offer the opportunity to perform more exhaustive assessments, including quantitative and qualitative analysis. The objectives of this study was to test method precision and accuracy, statistically compare bupivacaine drug concentration in real study samples and verify if high resolution and accurate mass data collected in scan mode can actually permit retrospective data analysis, more specifically, extract metabolite related information. The precision and accuracy data presented using both instruments provided equivalent results. Overall, the accuracy was ranging from 106.2 to 113.2% and the precision observed was from 1.0 to 3.7%. Statistical comparisons using a linear regression between both methods reveal a coefficient of determination (R2) of 0.9996 and a slope of 1.02 demonstrating a very strong correlation between both methods. Individual sample comparison showed differences from -4.5% to 1.6% well within the accepted analytical error. Moreover, post acquisition extracted ion chromatograms at m/z 233.1648 ± 5 ppm (M-56) and m/z 305.2224 ± 5 ppm (M+16) revealed the presence of desbutyl-bupivacaine and three distinct hydroxylated bupivacaine metabolites. Post acquisition analysis allowed us to produce semiquantitative evaluations of the concentration-time profiles for bupicavaine metabolites.

Assessment of meat authenticity using bioinformatics, targeted peptide biomarkers and high-resolution mass spectrometry

In recent years, we observed a significant increase of food fraud ranging from false label claims to the use of additives and fillers to increase profitability. Recently in 2013, horse and pig DNA were detected in beef products sold from several retailers. Mass spectrometry has become the workhorse in protein research and the detection of marker proteins could serve for both animal species and tissue authentication. Meat species authenticity will be performed using a well defined proteogenomic annotation, carefully chosen surrogate tryptic peptides and analysis using a hybrid quadrupole-Orbitrap mass spectrometer. Selected mammalian meat samples were homogenized, proteins were extracted and digested with trypsin. The samples were analyzed using a high-resolution mass spectrometer. The chromatography was achieved using a 30 minutes linear gradient along with a BioBasic C8 100 × 1 mm column at a flow rate of 75 µL/min. The mass spectrometer was operated in full-scan high resolution and accurate mass. MS/MS spectra were collected for selected proteotypic peptides. Muscular proteins were methodically analyzed in silico in order to generate tryptic peptide mass lists and theoretical MS/MS spectra. Following a comprehensive bottom-up proteomic analysis, we were able to detect and identify a proteotypic myoglobin tryptic peptide [120-134] for each species with observed m/z below 1.3 ppm compared to theoretical values. Moreover, proteotypic peptides from myosin-1, myosin-2 and -hemoglobin were also identified. This targeted method allowed a comprehensive meat speciation down to 1% (w/w) of undesired product.