The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

In recent years, research in the space community has shown a growing interest in Artificial
Intelligence (AI), mostly driven by systems miniaturization and commercial competition. In …

In the recent past, research on the utilization of deep learning algorithms for space
applications has been widespread. One of the areas where such algorithms are gaining …

In recent years, Convolutional Neural Networks (CNNs) have demonstrated outstanding
results in several emerging classification tasks. The high-quality predictions are often …

Edge devices offer advantages such as low computation latency and high data security for
executing convolutional neural networks (CNNs). However, deploying CNNs on resource …

To meet the increasing computing needs of various application fields, Field programmable
gate array (FPGA) has been widely deployed. In FPGA‐based processing, hardware tasks …

Since off-chip DRAM access affects both performance and power consumption significantly,
convolutional neural network (CNN) accelerators commonly aim to maximize data reuse in …

In recent years, FPGAs have demonstrated remarkable performance and contained power
consumption for the on-the-edge inference of Convolutional Neural Networks. One of the …

Reconfigurable nature of embedded FPGAs (EFPGA) are commonly used hardware for
system-on-chip design due to additional features of upgradability, and security with high …