The fast development of object detection techniques has attracted attention to developing
efficient Deep Neural Networks (DNNs). However, the current state-of-the-art DNN models …

Convolutional neural networks (CNNs) are widely used and have achieved great success in
computer vision and speech processing applications. However, deploying the large-scale …

We present a hardware-efficient architecture of convolutional neural network, which has a
repvgg-like architecture. Flops or parameters are traditional metrics to evaluate the efficiency …

Object recognition presents considerable difficulties within the domain of computer vision.
Field-Programmable Gate Arrays (FPGAs) offer a flexible hardware platform, having …

Abstract Convolutional Neural Network (CNN) has been extensively used for image
recognition due to its great accuracy. This accuracy is achieved through emulating the optic …

Convolutional Neural Networks (CNNs) can achieve high classification accuracy while they
require complex computation. Binarized Neural Networks (BNNs) with binarized weights …

Convolutional neural networks (CNNs) are widely used in many computer vision
applications. Previous FPGA implementations of CNNs are mainly based on the …

Neural network accelerators with low latency and low energy consumption are desirable for
edge computing. To create such accelerators, we propose a design flow for accelerating the …

In recent years, convolutional neural networks (CNNs) have become widely adopted for
computer vision tasks. Field-programmable gate arrays (FPGAs) have been adequately …

Being able to learn from complex data with phase information is imperative for many signal
processing applications. Today's real-valued deep neural networks (DNNs) have shown …