Abstract “Recurrent neural networks”(RNNs) are powerful artificial intelligence models that
have shown remarkable effectiveness in several tasks such as music generation, speech …

Abstract Recently, Deep Learning (DL) applications are getting more and more involved in
different fields. Deploying such Deep Neural Networks (DNN) on embedded devices is still a …

In limited-resource edge computing circumstances such as on mobile devices, IoT devices,
and electric vehicles, the energy-efficient optimized convolutional neural network (CNN) …

Convolution neural networks (CNN), support vector machine (SVM) and hybrid CNN-SVM
algorithms are widely applied in many fields, including image processing and fault …

Scene text detection network, such as Connectionist Text Proposal Network (CTPN), takes
CNN-RNN hybrid neural network as the main body, which can effectively recognize the text …

In recent years, radar emitter signal recognition has enjoyed a wide range of applications in
electronic support measure systems and communication security. More and more deep …

Recurrent neural networks (RNNs) are extensively employed to perform inference based on
the temporal features of the input data. However, their computational workload and power …

Recently, Convolution Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and
CNN-RNN hybrid networks have demonstrated great success in many deep learning …

This study presents a resource-efficient reconfigurable inference processor for recurrent
neural networks (RNN), named AERO. AERO is programmable to perform inference on RNN …

The vision chip is widely used to acquire and process images. It connects the image sensor
directly with the vision processing unit (VPU) to execute the vision tasks. Modern vision tasks …