Search
Now showing items 1-5 of 5
Benchmarking Object Detection Deep Learning Models in Embedded Devices
(MDPI, 2022)
Object detection is an essential capability for performing complex tasks in robotic applications. Today, deep learning (DL) approaches are the basis of state-of-the-art solutions in computer vision, where they provide very ...
An efficient implementation of kernel density estimation for multi-core and many-core architectures
(Sage, 2015-03-16)
Kernel density estimation (KDE) is a statistical technique used to estimate the probability density function of a sample set with unknown density function. It is considered a fundamental data-smoothing problem for use with ...
A Survey of Performance Modeling and Simulation Techniques for Accelerator-Based Computing
(IEEE, 2014-02-25)
The high performance computing landscape is shifting from collections of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator ...
Kernel density estimation in accelerators: Implementation and performance evaluation
(ACM, 2016-02-01)
Kernel density estimation (KDE) is a popular technique used to estimate the probability density function of a random variable. KDE is considered a fundamental data smoothing algorithm, and it is a common building block in ...
Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
(2015-01-01)
We propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to ...