727 research outputs found
Characterisation for Fine-Grain Reconfigurable Fabrics
This paper proposes a benchmarking methodology for characterising the power consumption of the fine-grain fabric in reconfigurable architectures. This methodology is part of the GroundHog 2009 power benchmarking suite. It covers active and inactive power as well as advanced low-power modes. A method based on random number generators is adopted for comparing activity modes. We illustrate our approach using five field-programmable gate arrays (FPGAs) that span a range of process technologies: Xilinx Virtex-II Pro, Spartan-3E, Spartan-3AN, Virtex-5, and Silicon Blue iCE65. We find that, despite improvements through process technology and low-power modes, current devices need further improvements to be sufficiently power efficient for mobile applications. The Silicon Blue device demonstrates that performance can be traded off to achieve lower leakage
statistical framework for dimensionality reduction implementation in fpgas
Abstract-Dimensionality reduction or feature extraction has been widely used in applications that require a set of data to be represented by a small set of variables. A linear projection is often chosen due to its computational attractiveness. The calculation of the linear basis that best explains the data is usually addressed using the Karhunen-Loeve Transform (KLT). Moreover, for applications where real-time performance and flexibility to accommodate new data are required, the linear projection is implemented in FPGAs due to their fine-grain parallelism and reconfigurability properties. Currently, the optimization of such a design in terms of area usage is considered as a separate problem to the basis calculation. In this paper, we propose a novel approach that couples the calculation of the linear projection basis and the area optimization problems under a probabilistic Bayesian framework. The power of the proposed framework is based on the flexibility to insert information regarding the implementation requirements of the linear basis by assigning a proper prior distribution. Results using real-life examples demonstrate the effectiveness of our approach
The Regulation of Actin Organization by Actin-Depolymerizing Factor in Elongating Pollen Tubes
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic
Modern CNN are typically based on floating point linear algebra based
implementations. Recently, reduced precision NN have been gaining popularity as
they require significantly less memory and computational resources compared to
floating point. This is particularly important in power constrained compute
environments. However, in many cases a reduction in precision comes at a small
cost to the accuracy of the resultant network. In this work, we investigate the
accuracy-throughput trade-off for various parameter precision applied to
different types of NN models. We firstly propose a quantization training
strategy that allows reduced precision NN inference with a lower memory
footprint and competitive model accuracy. Then, we quantitatively formulate the
relationship between data representation and hardware efficiency. Our
experiments finally provide insightful observation. For example, one of our
tests show 32-bit floating point is more hardware efficient than 1-bit
parameters to achieve 99% MNIST accuracy. In general, 2-bit and 4-bit fixed
point parameters show better hardware trade-off on small-scale datasets like
MNIST and CIFAR-10 while 4-bit provide the best trade-off in large-scale tasks
like AlexNet on ImageNet dataset within our tested problem domain.Comment: Accepted by ARC 201
TFTR 60 GHz alpha particle collective Thomson scattering diagnostic
A 60 GHz gyrotron collective Thomson Scattering alpha particle diagnostic has been implemented for the D-T period on TFM. Gyrotron power of 0.1-1 kW in pulses of up to 1 second can be launched in X-mode. Efficient corrugated waveguides are used with antennaes and vacuum windows of the TFTR Microwave Scattering system. A multichannel synchronous detector receiver system and spectrum analyzer acquire the scattered signals. A 200 Megasample/sec digitizer is used to resolve fine structure in the frequency spectrum. By scattering nearly perpendicular to the magnetic field, this experiment will take advantage of an enhancement of the scattered signal which results from the interaction of the alpha particles with plasma resonances in the lower hybrid frequency range. Significant enhancements are expected, which will make these measurements possible with gyrotron power less than 1 kW, while maintaining an acceptable signal to noise ratio. We hope to extract alpha particle density and velocity distribution functions from the data. The D and T fuel densities and temperatures may also be obtainable by measurement of the respective ion cyclotron harmonic frequencies
What has finite element analysis taught us about diabetic foot disease and its management?:a systematic review
Over the past two decades finite element (FE) analysis has become a popular tool for researchers seeking to simulate the biomechanics of the healthy and diabetic foot. The primary aims of these simulations have been to improve our understanding of the foot's complicated mechanical loading in health and disease and to inform interventions designed to prevent plantar ulceration, a major complication of diabetes. This article provides a systematic review and summary of the findings from FE analysis-based computational simulations of the diabetic foot.A systematic literature search was carried out and 31 relevant articles were identified covering three primary themes: methodological aspects relevant to modelling the diabetic foot; investigations of the pathomechanics of the diabetic foot; and simulation-based design of interventions to reduce ulceration risk.Methodological studies illustrated appropriate use of FE analysis for simulation of foot mechanics, incorporating nonlinear tissue mechanics, contact and rigid body movements. FE studies of pathomechanics have provided estimates of internal soft tissue stresses, and suggest that such stresses may often be considerably larger than those measured at the plantar surface and are proportionally greater in the diabetic foot compared to controls. FE analysis allowed evaluation of insole performance and development of new insole designs, footwear and corrective surgery to effectively provide intervention strategies. The technique also presents the opportunity to simulate the effect of changes associated with the diabetic foot on non-mechanical factors such as blood supply to local tissues.While significant advancement in diabetic foot research has been made possible by the use of FE analysis, translational utility of this powerful tool for routine clinical care at the patient level requires adoption of cost-effective (both in terms of labour and computation) and reliable approaches with clear clinical validity for decision making
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