Abstract—This paper presents an accelerator for k-th nearest neighbor thinning, a run time intensive algorithmic kernel used in recent multi-objective optimizers. We discuss the thinning algorithm and the accelerator architecture with its modules and operation, and evaluate the accelerator with respect to two different application scenarios. The first is an embedded computing scenario where the accelerator core is part of a configurable system-on-chip implemented on a modern platform FPGA. We show the resource requirements for different instances of the accelerator and report on the raw speedups achieved, which are up to 358x. The second scenario is in high performance computing where we map the accelerator core to a cutting-edge reconfigur...
In this paper, a configurable many-core hardware/ software architecture is proposed to efficiently ...
International audienceFPGA devices have been proving to be good candidates to accelerate application...
Hardware accelerators have become permanent features in the post-Dennard computing landscape, displa...
K-Nearest Neighbor (kNN) is an efficient algorithm used in many applications, e.g., text categorizat...
Field Programmable Gate Arrays (FPGAs) have been widely used for accelerating machine learning algor...
Image thinning algorithms are widely used in image processing to simplify elaboration preserving geo...
Fast and energy efficient processing of data has always been a key requirement in processor design. ...
Hardware accelerators such as GPUs and FPGAs can often provide enormous computing capabilities and p...
Low-power, high-performance computing nowadays relies on accelerator cards to speed up the calculati...
The design and implementation of the k-means clustering algorithm on an FPGA-accelerated computer cl...
There is a trend towards using accelerators to increase performance and energy efficiency of general...
Summarization: Important design considerations for the cost-effective employment of hardware acceler...
With the rapid development of the Internet of things (IoT), networks, software, and computing platfo...
In recent years, FPGAs have demonstrated remarkable performance and contained power consumption for ...
In recent years, FPGAs have demonstrated remarkable performance and contained power consumption for ...
In this paper, a configurable many-core hardware/ software architecture is proposed to efficiently ...
International audienceFPGA devices have been proving to be good candidates to accelerate application...
Hardware accelerators have become permanent features in the post-Dennard computing landscape, displa...
K-Nearest Neighbor (kNN) is an efficient algorithm used in many applications, e.g., text categorizat...
Field Programmable Gate Arrays (FPGAs) have been widely used for accelerating machine learning algor...
Image thinning algorithms are widely used in image processing to simplify elaboration preserving geo...
Fast and energy efficient processing of data has always been a key requirement in processor design. ...
Hardware accelerators such as GPUs and FPGAs can often provide enormous computing capabilities and p...
Low-power, high-performance computing nowadays relies on accelerator cards to speed up the calculati...
The design and implementation of the k-means clustering algorithm on an FPGA-accelerated computer cl...
There is a trend towards using accelerators to increase performance and energy efficiency of general...
Summarization: Important design considerations for the cost-effective employment of hardware acceler...
With the rapid development of the Internet of things (IoT), networks, software, and computing platfo...
In recent years, FPGAs have demonstrated remarkable performance and contained power consumption for ...
In recent years, FPGAs have demonstrated remarkable performance and contained power consumption for ...
In this paper, a configurable many-core hardware/ software architecture is proposed to efficiently ...
International audienceFPGA devices have been proving to be good candidates to accelerate application...
Hardware accelerators have become permanent features in the post-Dennard computing landscape, displa...