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 …

We evaluate the power and performance of the Rodinia benchmark suite using the Altera
SDK for OpenCL targeting a Stratix V FPGA against a modern CPU and GPU. We study …

FPGA-based accelerators have recently evolved as strong competitors to the traditional GPU-
based accelerators in modern high-performance computing systems. They offer both high …

Long Short-Term Memory (LSTM) networks have been widely used to solve sequence
modeling problems. For researchers, using LSTM networks as the core and combining it …

Abstract Field Programmable Gate Arrays (FPGAs) are integrated circuits that can be
configured by the user after manufacturing, making them suitable for customized hardware …

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 …

This paper presents kNN STreaming Unit For Fpgas (kNN-STUFF), a modular, scalable and
efficient Hardware/Software implementation of k-Nearest Neighbors (kNN) classifier …

Machine learning algorithms have become a major tool in various applications. The high-
performance requirements on large-scale datasets pose a challenge for traditional von …

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 …

Similarity search or nearest neighbor search is a task of retrieving a set of vectors in the
(vector) database that are most similar to the provided query vector. It has been a key kernel …