Object detection is one of the most challenging tasks in computer vision. With the advances
in semiconductor devices and chip technology, hardware accelerators have been widely …

Convolutional neural network (CNN) based object detection algorithms are becoming
dominant in many application fields due to their superior accuracy advantage over …

Abstract The concept of Convolution Neural Network (ConvNet or CNN) is evaluated from
the animal visual cortex. Since humans can learn through experience, similarly, ConvNet …

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 …

Unstructured deep neural network (DNN) pruning have been widely studied. However,
previous schemes only focused upon compressing the model's memory footprint, which had …

Convolutional Neural Networks (CNNs) have a major impact on our society, because of the
numerous services they provide. These services include, but are not limited to image …

Deep Neural Networks (DNNs) are currently used in many fields, including critical real-time
applications. Due to its compute-intensive nature, speeding up DNNs has become an …

Federated learning has solved the problems of data silos and data fragmentation on the
premise of satisfying privacy. However, cryptographic algorithms in federated learning …

In recent years, dedicated hardware accelerators for the acceleration of the convolutional
neural network (CNN) have been extensively studied. Although many studies have …

Field-programmable gate array (FPGA)-based accelerators for convolutional neural network
(CNN) inference have received significant attention in recent years. The reported designs …