Abstract: Convolution neural networks (CNNs) have been extensively used in machine learning applications. The most time-consuming part of CNNs are convolution operations. A common approach to ...

Abstract: In this work, we propose a multiplication-less deep convolution neural network, called BD-NET. As far as we know, BD-NET is the first to use binarized depthwise separable convolution block ...

2D Convolution: Wrote a custom implementation of image convolution in Assembly and compared its execution time to a Python implementation. The goal was to demonstrate how low-level memory control and ...

A high-performance hardware accelerator designed for image convolution operations in Convolutional Neural Networks (CNNs). This implementation provides an efficient FPGA-based solution for ...

Using the combinatorics of non-crossing partitions, we construct a conditionally free analogue of Voiculescu's S-transform. The result is applied to the analytical description of conditionally free ...

A collaborative team from the Institute for Basic Science (IBS), Yonsei University, and the Max Planck Institute has developed a new artificial intelligence (AI) technique called Lp-Convolution. This ...

This is a preview. Log in through your library . Abstract The semi-discrete convolution with the box spline is an important tool in approximation theory. We give a formula for the difference between ...