We propose here an implementation of 2D hierarchical clustering tailored for power constrained and low-precision hardware. In many application fields such as smart sensor networks, having low computational capacity is mandatory for energy saving purposes. In this context, we aim to deploy a specific constrained hardware solution, using a parallel architecture with a low number of bits. The effectiveness of the proposed approach is corroborated by testing it on well-known 2D clustering datasets. Numerical results show how our low power solution can be implemented without affecting clustering accuracy
Graphics processing units (GPUs) are powerful com-putational devices tailored towards the needs of t...
Energy efficient modeling is a major issue in the wireless sensor network. The main solution for ene...
Non-hierarchical k-means algorithms have been implemented in hardware, most frequently for image clu...
In this paper we propose a novel hardware implementation for a bidimensional unconstrained hierarchi...
In this paper we propose a novel hardware implementation for a bidimensional unconstrained hierarchi...
A multi-core FPGA-based clustering algorithm for high-throughput data intensive applications is pres...
The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer dec...
The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer dec...
In the hierarchical wireless sensor network (WSN), cluster-based network architecture can enhance ne...
In this work, we introduce the Multilevel Hierarchical Clustering (MLHC-m) algorithm, which is a new...
Organizing data into groups using unsupervised learning algorithms such as k-means clustering and GM...
We present a fast general-purpose algorithm for high-throughput clustering of data ”with a two dimen...
International audienceFPGA devices have been proving to be good candidates to accelerate application...
Hierarchical clustering is a fundamental and widely-used clustering algorithm with many advantages o...
Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applicat...
Graphics processing units (GPUs) are powerful com-putational devices tailored towards the needs of t...
Energy efficient modeling is a major issue in the wireless sensor network. The main solution for ene...
Non-hierarchical k-means algorithms have been implemented in hardware, most frequently for image clu...
In this paper we propose a novel hardware implementation for a bidimensional unconstrained hierarchi...
In this paper we propose a novel hardware implementation for a bidimensional unconstrained hierarchi...
A multi-core FPGA-based clustering algorithm for high-throughput data intensive applications is pres...
The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer dec...
The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer dec...
In the hierarchical wireless sensor network (WSN), cluster-based network architecture can enhance ne...
In this work, we introduce the Multilevel Hierarchical Clustering (MLHC-m) algorithm, which is a new...
Organizing data into groups using unsupervised learning algorithms such as k-means clustering and GM...
We present a fast general-purpose algorithm for high-throughput clustering of data ”with a two dimen...
International audienceFPGA devices have been proving to be good candidates to accelerate application...
Hierarchical clustering is a fundamental and widely-used clustering algorithm with many advantages o...
Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applicat...
Graphics processing units (GPUs) are powerful com-putational devices tailored towards the needs of t...
Energy efficient modeling is a major issue in the wireless sensor network. The main solution for ene...
Non-hierarchical k-means algorithms have been implemented in hardware, most frequently for image clu...