Abstract: The quantitative evaluation of defects is one of the challenges of eddy current (EC) testing. Efficient algorithm that requires a small amount of time and hardware resources is needed. In ...

Abstract: In recent years, FPGA-based convolutional neural networks (CNNs) accelerator has received tremendous research interest, especially in fields such as autonomous driving and robotics. For the ...

This document serves as a log of the progress and knowledge I gained while working on GPU programming and studying the PMPP (Parallel Programming and Optimization) book. Implemented vector addition by ...

A convolutional neural network (CNN) is a category of machine learning model. Specifically, it is a type of deep learning algorithm that is well suited to analyzing visual data. CNNs are commonly used ...

ABSTRACT: The development of new technologies in smart cities is often hailed as it becomes a necessity to solve many problems like energy consumption and transportation. Wireless networks are part of ...

The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically ...

In tackling the intricate task of predicting brain age, researchers introduce a groundbreaking hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Multilayer Perceptron ...

error in inference process (no valid convolution algorithms available in CuDNN) #25 New issue ...

ABSTRACT: Spam emails pose a threat to individuals. The proliferation of spam emails daily has rendered traditional machine learning and deep learning methods for screening them ineffective and ...

Reasoning efficiently across extended sequences is a major difficulty in machine learning. Recently, convolutions have emerged as a critical primitive for sequence modeling, supporting ...