The computational efficiency is the prime concern of a computation-intensive deep
convolutional neural network (CNN). In this Brief, we report an FPGA-based computation …

The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …

The emergence of hardware accelerators has brought about several orders of magnitude
improvement in the speed of the deep neural-network (DNN) inference. Among such DNN …

Currently, there is a growing attention toward developing efficient hardware convolutional
blocks for several applications such as computer vision or image processing. Recent works …

The softmax function is widely used in deep neural networks (DNNs), its hardware
performance plays an important role in the training and inference of DNN accelerators …

Deep learning has become the main solution for face recognition applications due to its high
accuracy and robustness. In recent years, a batch of research on lightweight convolutional …

With data-driven analytics becoming mainstream, the global demand for dedicated artificial
intelligence (AI) and deep learning accelerator chips is soaring. These accelerators …

Hardware faults on the regular 2-D computing array of a typical deep learning accelerator
(DLA) can lead to dramatic prediction accuracy loss. Prior redundancy design approaches …

Ferroelectric FET (FeFET) is a highly promising emerging non-volatile memory (NVM)
technology, especially for binarized neural network (BNN) inference on the low-power edge …

Abstract Convolutional Neural Networks (CNN) are being actively explored for safety-critical
applications such as autonomous vehicles and aerospace, where it is essential to ensure …