FPGA implementations of SVM classifiers: A review
Support vector machine (SVM) is a robust machine learning model with high classification
accuracy. SVM is widely utilized for online classification in various real-time embedded …
accuracy. SVM is widely utilized for online classification in various real-time embedded …
[HTML][HTML] Optimized implementation of an improved KNN classification algorithm using Intel FPGA platform: Covid-19 case study
The improved k-nearest neighbor (KNN) algorithm based on class contribution and feature
weighting (DCT-KNN) is a highly accurate approach. However, it requires complex …
weighting (DCT-KNN) is a highly accurate approach. However, it requires complex …
[HTML][HTML] Adaptive approximate computing in edge AI and IoT applications: A review
Recent advancements in hardware and software systems have been driven by the
deployment of emerging smart health and mobility applications. These developments have …
deployment of emerging smart health and mobility applications. These developments have …
Accelerating kNN search in high dimensional datasets on FPGA by reducing external memory access
Implementing an efficient k-Nearest Neighbors (kNN) algorithm on FPGA is becoming
challenging due to the fact that both the size and dimensionality of datasets that kNN is …
challenging due to the fact that both the size and dimensionality of datasets that kNN is …
kNN-STUFF: KNN streaming unit for Fpgas
This paper presents kNN STreaming Unit For Fpgas (kNN-STUFF), a modular, scalable and
efficient Hardware/Software implementation of k-Nearest Neighbors (kNN) classifier …
efficient Hardware/Software implementation of k-Nearest Neighbors (kNN) classifier …
CHIP-KNN: A configurable and high-performance k-nearest neighbors accelerator on cloud FPGAs
The k-nearest neighbors (KNN) algorithm is an essential algorithm in many applications,
such as similarity search, image classification, and database query. With the rapid growth in …
such as similarity search, image classification, and database query. With the rapid growth in …
CHIP-KNNv2: AC onfigurable and Hi gh-P erformance KN earest N eighbors Accelerator on HBM-based FPGAs
The k-nearest neighbors (KNN) algorithm is an essential algorithm in many applications,
such as similarity search, image classification, and database query. With the rapid growth in …
such as similarity search, image classification, and database query. With the rapid growth in …
Fast neuromimetic object recognition using FPGA outperforms GPU implementations
Recognition of objects in still images has traditionally been regarded as a difficult
computational problem. Although modern automated methods for visual object recognition …
computational problem. Although modern automated methods for visual object recognition …
A memory-access-efficient adaptive implementation of kNN on FPGA through HLS
To reduce the impact of the memory-access constraint in k-Nearest Neighbors (kNN)
problems, in this paper we implement one kNN kernel through high-level synthesis (HLS) on …
problems, in this paper we implement one kNN kernel through high-level synthesis (HLS) on …
K-nearest neighbor algorithm implementation on FPGA using high level synthesis
ZH Li, JF Jin, XG Zhou, ZH Feng - 2016 13th IEEE International …, 2016 - ieeexplore.ieee.org
The K-Nearest Neighbor (K-NN) algorithm is one of the most common classification
algorithms and widely used in pattern recognition and data mining. K-NN hardware …
algorithms and widely used in pattern recognition and data mining. K-NN hardware …