A case study for NoC based homogeneous MPSoC architectures

The many-core design paradigm requires flexible and modular hardware and software components to provide the required scalability to next-generation on-chip multiprocessor architectures. A multidisciplinary approach is necessary to consider all the interactions between the different components of the design. In this paper, a complete design methodology that tackles at once the aspects of system level modeling, hardware architecture, and programming model has been successfully used for the implementation of a multiprocessor network-on-chip (NoC)-based system, the NoCRay graphic accelerator. The design, based on 16 processors, after prototyping with field-programmable gate array (FPGA), has been laid out in 90-nm technology. Post-layout results show very low power, area, as well as 500 MHz of clock frequency. Results show that an array of small and simple processors outperform a single high-end general purpose processo

On a self-sustained process at large scale in the turbulent channel flow

Large-scale motions, important in turbulent shear flows, are frequently attributed to the interaction of structures at smaller scale. Here we show that, in a turbulent channel at Re_{\tau} \approx 550, large-scale motions can self-sustain even when smaller-scale structures populating the near-wall and logarithmic regions are artificially quenched. This large-scale self-sustained mechanism is not active in periodic boxes of width smaller than Lz ~ 1.5h or length shorter than Lx ~ 3h which correspond well to the most energetic large scales observed in the turbulent channel

Organic agriculture and climate change mitigation - A report of the Round Table on Organic Agriculture and Climate Change

Summary and next steps Participants of the workshop were able to draw from their discussions and from the input of guest speakers and synthesize a set of conclusions that can be used to guide future activities concerning LCAs and other activities that seek to identify and quantify the potential contributions of organic agriculture to climate change mitigation. - LCA is the best tool for measuring GHG emissions related to agricultural products. - There is a risk of oversimplification when focusing on climate change as a single environmental impact category. - Farm production and transport (at least for plant products) are important hotspots for agricultural products. - Studies have shown no remarkable difference in GHG emissions between organic and conventional but, traditionally, soil carbon changes have not been included – which can have a major impact, especially for plant products. - The challenges of LCA of organic products – accounting for carbon sequestration and interactions in farming systems, including the environmental costs of manure – need to be addressed. - Attempts should be made to secure a consistent LCA methodology for agricultural products, including organic products

Tunable sensor response by voltage-control in biomimetic hair flow sensors

We present an overview of improvements in detection limit and responsivity of our biomimetic hair flow sensors by electrostatic spring-softening (ESS). Applying a DC-bias voltage to our capacitive flow sensors improves the responsively by up to 80% for flow signals at frequencies below the sensor’s resonance. Application of frequency matched AC-bias voltages allows for tunable filtering and selective gain up to 20 dB. Furthermore, the quality and fidelity of low frequency flow measurements can be improved using a non frequency-matched AC-bias voltage, resulting in a flow detection limit down to 5 mm/s at low (30 Hz) frequencies. The merits and applicability of the three methods are discussed

Measuring delays for bicycles at signalized intersections using smartphone GPS tracking data

The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values

Convection and chemistry effects in CVD: A 3-D analysis for silicon deposition

The computational fluid dynamics code FLUENT has been adopted to simulate the entire rectangular-channel-like (3-D) geometry of an experimental CVD reactor designed for Si deposition. The code incorporated the effects of both homogeneous (gas phase) and heterogeneous (surface) chemistry with finite reaction rates of important species existing in silane dissociation. The experiments were designed to elucidate the effects of gravitationally-induced buoyancy-driven convection flows on the quality of the grown Si films. This goal is accomplished by contrasting the results obtained from a carrier gas mixture of H2/Ar with the ones obtained from the same molar mixture ratio of H2/He, without any accompanying change in the chemistry. Computationally, these cases are simulated in the terrestrial gravitational field and in the absence of gravity. The numerical results compare favorably with experiments. Powerful computational tools provide invaluable insights into the complex physicochemical phenomena taking place in CVD reactors. Such information is essential for the improved design and optimization of future CVD reactors

Towards the identification of spatially resolved mechanical properties in tissues and materials: State of the art, current challenges and opportunities in the field of flow measurements

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.This work is focused on optical methods that provide tomographic reconstructions of the structure of materials and tissues. Phase information can also be used to measure 3-D displacement and strain fields with interferometric sensitivity. Different approaches are presented, including recent developments in phase contrast wavelength scanning interferometry and a combination of optical coherence tomography and digital volume correlation to estimate elastic properties of synthetic phantoms and porcine corneas. Inversion algorithms based on finite elements and the Virtual Fields Method (VFM) are used to extract mechanical properties from the knowledge of the applied loads, geometry and measured deformation fields. Current efforts into extending these methods into single shot techniques have the potential of expanding the range of applications to study dynamic events such as micro-flows in engineering and biological systems in which scattering particles are transported in a flow, e.g. tribology, microfluidic devices, cell migration or multiphase flows

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Data-dependent cycle-accurate power modeling of RTL-level IPs using machine learning

In a chip design project, early design planning has a strong impact on the schedule and the cost of design. Power estimation is part of early design planning, and it greatly affects design decisions. Power modeling performed at a high level of abstraction is fast but inaccurate due to lack of circuit switching activity information. By contrast, power modeling performed at a low level of abstraction is more accurate as the synthesized circuit synthesis is known, but this simulation is typically slow. This report explores a power modeling approach performed at register transfer level (RTL). It exploits machine learning models in order to have a fast yet relatively accurate cycle-by-cycle power estimation. The approach is data-dependent, where cycle-specific models are trained based on the switching activity of signals obtained from RTL simulation and cycle-by-cycle power values obtained from a reference gate-level simulation of an existing RTL design. Therefore, if any changes are applied to the RTL design, re-training of models is required. The approach aims at obtaining fast yet accurate power predictions for new invocations of a given trained model using signal activity information collected during simulation of the unmodified RTL. At a low level, the complete visibility of signals in a design unintuitively might cause overtraining the model leading to inaccurate estimation. The suggested model employs automatic feature selection in each cycle. Based on the invocations used to train the cycle-by-cycle models, only signals that may switch during a given cycle will be selected as the features for their respective cycle-specific model. The method was tested on an 8-by-8 DCT design and the power estimates were within 6.5% of those from a commercial power analysis tool. This report also simulates and compares the approach of cycle-specific models to the approach of a single global model for all cycles and show that the cycle-specific approach is twice as accurate.Electrical and Computer Engineerin

A High-level Methodology for Automatically Generating Dynamic Partially Reconfigurable Systems using IP-XACT and the UML MARTE Profile

International audienceDynamic Partial Reconfiguration (DPR) has been introduced in recent years as a method to increase the flexibility of FPGA designs. However, using DPR for building com- plex systems remains a daunting task. Recently, approaches based on Model-Driven Engi- neering (MDE) and UML MARTE standard have emerged which aim to simplify the design of complex SoCs, and in some cases, DPR systems. Nevertheless, many of these approaches lacked a standard intermediate representation to pass from high-levels of descriptions to ex- ecutable models. However, with the recent standardization of the IP-XACT specification, there is an increasing interest to use it in MDE methodologies to ease system integration and to enable design flow automation. In this paper we propose an MARTE/MDE approach which exploits the capabilities of IP-XACT to model and automatically generate DPR SoC designs. We present the MARTE modeling concepts and how these models are mapped to IP-XACT objects; the emphasis is given to the generation of IP cores that can be used in the Xilinx EDK (Embedded Design Kit) environment, since we aim to develop a complete flow around their Dynamic Partial Reconfiguration design flow. Finally, we present a case study integrating the presented concepts, showing the benefits in design efforts compared with a purely VHDL approach and using solely EDK. Experimental results show a reduction of the design efforts required to obtain the netlist required for the DPR design flow from hours required in VHDL and Xilinx EDK, to less the one hour and minutes for IP integration