Accelerating deep learning networks in edge computing based on power-efficient and highly
parallel FPGA platforms is an important goal. Combined with deep learning theory, an …

In this article, we analyze algorithmic ways to reduce the arithmetic complexity of calculating
quaternion-valued linear convolution and also synthesize a new algorithm for calculating …

为减少卷积神经网络(CNN) 的计算量, 该文将2 维快速滤波算法引入到卷积神经网络,
并提出一种在FPGA 上实现CNN 逐层加速的硬件架构. 首先, 采用循环变换方法设计行缓存循环 …

The paper introduces a range of efficient algorithmic solutions for implementing the
fundamental filtering operation in convolutional layers of convolutional neural networks on …

In this article, we propose a set of efficient algorithmic solutions for computing short linear
convolutions focused on hardware implementation in VLSI. We consider convolutions for …

In order to reduce the computational complexity of Convolutional Neural Network (CNN), the
two-dimensional fast filtering algorithm is introduced into the CNN, and a hardware …

The inference speed of convolutional neural networks is a key factor for their application in
the industry. The two research directions of non-sparse model acceleration and sparse …

Rapid advancements in science and technology has paved path for researches in
developing machines which have faster processing capabilities than human brain. Image …

The goal of automated troubleshooting is to diagnose and fix a system by repairing its
abnormal behaviors, and this process has wide application. Heuristic search is an efficient …