The potential of machine learning for a more responsible sourcing of critical raw materials
The digitization and automation of the raw material sector is required to attain the targets set
by the Paris Agreements and support the sustainable development goals defined by the …
by the Paris Agreements and support the sustainable development goals defined by the …
Hyperspectral image clustering: Current achievements and future lines
Hyperspectral remote sensing organically combines traditional space imaging with
advanced spectral measurement technologies, delivering advantages stemming from …
advanced spectral measurement technologies, delivering advantages stemming from …
Fpga implementation of k-means algorithm for bioinformatics application: An accelerated approach to clustering microarray data
The Microarray is a technique used by biologists to perform many genome experiments
simultaneously, which produces very large datasets. Analysis of these datasets is a …
simultaneously, which produces very large datasets. Analysis of these datasets is a …
Heterogeneous hardware accelerator architecture for processing sparse matrix data with skewed non-zero distributions
E Nurvitadhi, D Marr - US Patent 10,180,928, 2019 - Google Patents
Heterogeneous hardware accelerator architectures for pro cessing sparse matrix data
having skewed non-zero distri butions are described. An accelerator includes sparse tiles to …
having skewed non-zero distri butions are described. An accelerator includes sparse tiles to …
Highly parameterized k-means clustering on fpgas: Comparative results with gpps and gpus
K-means clustering has been widely used in processing large datasets in many fields of
studies. Advancement in many data collection techniques has been generating enormous …
studies. Advancement in many data collection techniques has been generating enormous …
Map-reduce processing of k-means algorithm with FPGA-accelerated computer cluster
YM Choi, HKH So - 2014 IEEE 25th international conference …, 2014 - ieeexplore.ieee.org
The design and implementation of the k-means clustering algorithm on an FPGA-
accelerated computer cluster is presented. The implementation followed the Map-Reduce …
accelerated computer cluster is presented. The implementation followed the Map-Reduce …
Vfloat: A variable precision fixed-and floating-point library for reconfigurable hardware
X Wang, M Leeser - ACM Transactions on Reconfigurable Technology …, 2010 - dl.acm.org
Optimal reconfigurable hardware implementations may require the use of arbitrary floating-
point formats that do not necessarily conform to IEEE specified sizes. We present a variable …
point formats that do not necessarily conform to IEEE specified sizes. We present a variable …
Graph-Constrained Residual Self-Expressive Subspace Clustering Network for Hyperspectral Images
K Huang, X Li, Y Pi, H Cheng… - IEEE Journal of Selected …, 2023 - ieeexplore.ieee.org
Hyperspectral images are widely use due to their rich spectral information. Meanwhile, the
difficult acquisition of data labels makes unsupervised classification attracts attention …
difficult acquisition of data labels makes unsupervised classification attracts attention …
A fast and scalable FPGA-based parallel processing architecture for K-means clustering for big data analysis
R Raghavan, DG Perera - 2017 IEEE Pacific Rim Conference …, 2017 - ieeexplore.ieee.org
The exponential growth of complex, heterogeneous, dynamic, and unbounded data,
generated by a variety of fields including health, genomics, physics, climatology, and social …
generated by a variety of fields including health, genomics, physics, climatology, and social …
Accelerating apache spark with fpgas
Apache Spark has become one of the most popular engines for big data processing. Spark
provides a platform‐independent, high‐abstraction programming paradigm for large‐scale …
provides a platform‐independent, high‐abstraction programming paradigm for large‐scale …