Analytical Model for High–Level Area Estimation of FPGA Design

2016 ◽  
Vol 7 (2) ◽  
pp. 35-44
Author(s):  
Rachna Singh ◽  
Arvind Rajawat
Keyword(s):  
Early Stage ◽  
Estimation Error ◽  
Research Work ◽  
Performance Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Fpga Design ◽  
Estimation Model ◽  
Area Estimation ◽  
High Level

FPGAs have been used as a target platform because they have increasingly interesting in system design and due to the rapid technological progress ever larger devices are commercially affordable. These trends make FPGAs an alternative in application areas where extensive data processing plays an important role. Consequently, the desire emerges for early performance estimation in order to quantify the FPGA approach. A mathematical model has been presented that estimates the maximum number of LUTs consumed by the hardware synthesized for different FPGAs using LLVM.. The motivation behind this research work is to design an area modeling approach for FPGA based implementation at an early stage of design. The equation based area estimation model permits immediate and accurate estimation of resources. Two important criteria used to judge the quality of the results were estimation accuracy and runtime. Experimental results show that estimation error is in the range of 1.33% to 7.26% for Spartan 3E, 1.6% to 5.63% for Virtex-2pro and 2.3% to 6.02% for Virtex-5.

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

scholarly journals Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1785 ◽  
Author(s):  
Guoqing Jin ◽  
Lan Li ◽  
Yidan Xu ◽  
Minghui Hu ◽  
Chunyun Fu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fractional Order ◽  
Estimation Error ◽  
Stress Test ◽  
Operating Conditions ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Order Model ◽  
Soc Estimation ◽  
Fractional Order Model

Accurate estimation of the state of charge (SOC) is an important criterion to prevent the batteries from being over-charged or over-discharged, and this assures an electric vehicle’s safety and reliability. To investigate the effect of different operating conditions on the SOC estimation results, a dual-polarization model (DPM) and a fractional-order model (FOM) are established in this study, taking into account the prediction accuracy and structural complexity of a battery model. Based on these two battery equivalent circuit models (ECMs), a hybrid Kalman filter (HKF) algorithm is adopted to estimate the SOC of the battery; the algorithm comprehensively utilizes the ampere-hour (Ah) integration method, the Kalman filter (KF) algorithm, and the extended Kalman filter (EKF) algorithm. The SOC estimation results of the DPM and FOM, under the dynamic stress test (DST), federal urban driving schedule (FUDS), and hybrid pulse power characteristic (HPPC) cycle conditions, are compared and analyzed through six sets of experiments. Simulation results show that the SOC estimation accuracy of both the models is high and that the errors are within the range of ±0.06. Under any operating conditions, the SOC estimation error, based on the FOM, is always lower than the SOC estimation error of the DPM, but the adaptability of the FOM is not as high as that of the DPM.

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

scholarly journals Improving Performance Estimation for Design Space Exploration for Convolutional Neural Network Accelerators

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 520
Author(s):  
Martin Ferianc ◽  
Hongxiang Fan ◽  
Divyansh Manocha ◽  
Hongyu Zhou ◽  
Shuanglong Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Prediction Method ◽  
Gaussian Process Regression ◽  
Performance Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation

Contemporary advances in neural networks (NNs) have demonstrated their potential in different applications such as in image classification, object detection or natural language processing. In particular, reconfigurable accelerators have been widely used for the acceleration of NNs due to their reconfigurability and efficiency in specific application instances. To determine the configuration of the accelerator, it is necessary to conduct design space exploration to optimize the performance. However, the process of design space exploration is time consuming because of the slow performance evaluation for different configurations. Therefore, there is a demand for an accurate and fast performance prediction method to speed up design space exploration. This work introduces a novel method for fast and accurate estimation of different metrics that are of importance when performing design space exploration. The method is based on a Gaussian process regression model parametrised by the features of the accelerator and the target NN to be accelerated. We evaluate the proposed method together with other popular machine learning based methods in estimating the latency and energy consumption of our implemented accelerator on two different hardware platforms targeting convolutional neural networks. We demonstrate improvements in estimation accuracy, without the need for significant implementation effort or tuning.

scholarly journals UAV Remote Sensing Estimation of Rice Yield Based on Adaptive Spectral Endmembers and Bilinear Mixing Model

2021 ◽  
Vol 13 (11) ◽  
pp. 2190
Author(s):  
Ningge Yuan ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Yating Liu ◽  
Bo Duan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rice Yield ◽  
Estimation Error ◽  
Structural Difference ◽  
Mixing Model ◽  
Accurate Estimation ◽  
Endmember Extraction ◽  
Yield Estimation ◽  
Estimation Model ◽  
Heading Stage

The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs and background especially at heading stage. The spectral mixture analysis (SMA) can quantitatively obtain the abundance information and mitigate the impacts. Furthermore, according to the spectral variability and information complexity caused by the rice cropping system and canopy characteristics of reflection and scattering, in this study, the multi-endmember extraction by the pure pixel index (PPI) and the nonlinear unmixing method based on the bandwise generalized bilinear mixing model (NU-BGBM) were applied for SMA, and the VIE (VIs recalculated from endmember spectra) was integrated with abundance data to establish the yield estimation model at heading stage. In two paddy fields of different cultivation settings, multispectral images were collected by an unmanned aerial vehicle (UAV) at booting and heading stage. The correlation of several widely-used VIs and rice yield was tested and weaker at heading stage. In order to improve the yield estimation accuracy of rice at heading stage, the VIE and foreground abundances from SMA were combined to develop a linear yield estimation model. The results showed that VIE incorporated with abundances exhibited a better estimation ability than VI alone or the product of VI and abundances. In addition, when the structural difference of plants was obvious, the addition of the product of VIF (VIs recalculated from bilinear endmember spectra) and the corresponding bilinear abundances to the original product of VIE and abundances, enhanced model reliability. VIs using the near-infrared bands improved more significantly with the estimation error below 8.1%. This study verified the validation of the targeted SMA strategy while estimating crop yield by remotely sensed VI, especially for objects with obvious different spectra and complex structures.

scholarly journals Inherent Parallelism and Speedup Estimation of Sequential Programs

2021 ◽  
Vol 5 (2) ◽  
pp. 62-77
Author(s):  
Sesha Kalyur ◽  
Nagaraja G.S
Keyword(s):  
Research Work ◽  
Performance Estimation ◽  
Sequential Programs ◽  
Parallel Class ◽  
Parallel Performance ◽  
Effective Performance ◽  
Parallel Speedup ◽  
High Level ◽  
Performance Estimates ◽  
Parallel Estimation

Although several automated Parallel Conversion solutions are available, very few have attempted, to provide proper estimates of the available Inherent Parallelism and expected Parallel Speedup. CALIPER which is the outcome of this research work is a parallel performance estimation technology that can fill this void. High level language structures such as Functions, Loops, Conditions, etc which ease program development, can be a hindrance for effective performance analysis. We refer to these program structures as the Program Shape. As a preparatory step, CALIPER attempts to remove these shape related hindrances, an activity we refer to as Program Shape Flattening. Programs are also characterized by dependences that exist between different instructions and impose an upper limit on the parallel conversion gains. For parallel estimation, we first group instructions that share dependences, and add them to a class we refer to as Dependence Class or Parallel Class. While instructions belonging to a class run sequentially, the classes themselves run in parallel. Parallel runtime, is now the runtime of the class that runs the longest. We report performance estimates of parallel conversion as two metrics. The inherent parallelism in the program is reported, as Maximum Available Parallelism (MAP) and the speedup after conversion as Speedup After Parallelization (SAP).

scholarly journals Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 324
Author(s):  
Haobin Jiang ◽  
Xijia Chen ◽  
Yifu Liu ◽  
Qian Zhao ◽  
Huanhuan Li ◽  
...  
Keyword(s):  
Stress Test ◽  
Robustness Analysis ◽  
Open Circuit ◽  
Combined Cycle ◽  
State Of Charge ◽  
Constant Current ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Liquid Dynamics

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

Lung nodule classification using combination of CNN, second and higher order texture features

2021 ◽  
pp. 1-9
Author(s):  
Amrita Naik ◽  
Damodar Reddy Edla
Keyword(s):  
Malignant Tumors ◽  
Early Stage ◽  
Lung Nodule ◽  
Texture Features ◽  
Svm Classifier ◽  
Softmax Classifier ◽  
Spatial Features ◽  
High Level ◽  
Learning Architectures ◽  
Nodule Classification

Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.

Assessment of 2D ultrasound fluid volume estimation accuracy in different shaped objects: an in vitro study

2019 ◽  
Vol 61 (2) ◽  
pp. 253-259
Author(s):  
Iroshani Kodikara ◽  
Iroshini Abeysekara ◽  
Dhanusha Gamage ◽  
Isurani Ilayperuma
Keyword(s):  
Estimation Error ◽  
In Vitro Study ◽  
High Accuracy ◽  
Volume Estimation ◽  
Estimation Accuracy ◽  
Rater Agreement ◽  
Actual Volume ◽  
2D Ultrasound ◽  
One Way Anova

Background Volume estimation of organs using two-dimensional (2D) ultrasonography is frequently warranted. Considering the influence of estimated volume on patient management, maintenance of its high accuracy is empirical. However, data are scarce regarding the accuracy of estimated volume of non-globular shaped objects of different volumes. Purpose To evaluate the volume estimation accuracy of different shaped and sized objects using high-end 2D ultrasound scanners. Material and Methods Globular (n=5); non-globular elongated (n=5), and non-globular near-spherical shaped (n=4) hollow plastic objects were scanned to estimate the volumes; actual volumes were compared with estimated volumes. T-test and one-way ANOVA were used to compare means; P<0.05 was considered significant. Results The actual volumes of the objects were in the range of 10–445 mL; estimated volumes ranged from 6.4–425 mL ( P=0.067). The estimated volume was lower than the actual volume; such volume underestimation was marked for non-globular elongated objects. Regardless of the scanner, the highest volume estimation error was for non-globular elongated objects (<40%) followed by non-globular near-spherical shaped objects (<23.88%); the lowest was for globular objects (<3.6%). Irrespective of the shape or the volume of the object, volume estimation difference among the scanners was not significant: globular (F=0.430, P=0.66); non-globular elongated (F=3.69, P=0.064); and non-globular near-spherical (F=4.00, P=0.06). A good inter-rater agreement (R=0.99, P<0.001) and a good correlation between actual versus estimated volumes (R=0.98, P<0.001) were noted. Conclusion The 2D ultrasonography can be recommended for volume estimation purposes of different shaped and different sized objects, regardless the type of the high-end scanner used.

scholarly journals Categorification of the Müller-Wichards System Performance Estimation Model: Model Symmetries, Invariants, and Closed Forms

Systems ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 6
Author(s):  
Allen D. Parks ◽  
David J. Marchette
Keyword(s):  
Closed Form ◽  
System Performance ◽  
Algebraic Method ◽  
Expressive Power ◽  
Computer Applications ◽  
Performance Estimation ◽  
Parallel Computer ◽  
Estimation Model ◽  
Data Movement ◽  
Fundamental Symmetry

The Müller-Wichards model (MW) is an algebraic method that quantitatively estimates the performance of sequential and/or parallel computer applications. Because of category theory’s expressive power and mathematical precision, a category theoretic reformulation of MW, i.e., CMW, is presented in this paper. The CMW is effectively numerically equivalent to MW and can be used to estimate the performance of any system that can be represented as numerical sequences of arithmetic, data movement, and delay processes. The CMW fundamental symmetry group is introduced and CMW’s category theoretic formalism is used to facilitate the identification of associated model invariants. The formalism also yields a natural approach to dividing systems into subsystems in a manner that preserves performance. Closed form models are developed and studied statistically, and special case closed form models are used to abstractly quantify the effect of parallelization upon processing time vs. loading, as well as to establish a system performance stationary action principle.

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