In recent years, convolutional neural networks (CNNs) have gained widespread adoption in
remote sensing image processing. Deploying CNN-based algorithms on satellite edge …

Lightweight convolutional neural networks (CNNs) have become increasingly popular due
to their lower computational complexity and fewer memory accesses with equivalent …

Since the lightweight convolutional neural network EfficientNet was proposed by Google in
2019, the series of models have quickly become very popular due to their superior …

The hardware accelerator controlled by direct memory access (DMA) is greatly influenced by
the communication bandwidth from/to DRAM through on-chip buses. This paper proposes a …

Fine-grained sparse convolutional neural networks (CNNs) achieve a better trade-off
between model accuracy and size than coarse-grained sparse CNNs. Due to irregular data …

Multicore accelerators have emerged to efficiently execute recent applications with complex
computational dimensions. Compared to a single-core accelerator, a multicore accelerator …

Neural networks are now routinely used for perception processing in autonomous systems.
Often, these neural networks are used to estimate the state of the system, such as distance …

With the development of lightweight convolutional neural networks (CNNs), these newly
proposed networks are more powerful than previous conventional models [,] and can be well …

Abstract Deep Neural Networks (DNN) form the backbone of modern Artificial Intelligence
(AI) systems. However, due to the high computational complexity and divergent shapes and …

Abstract Deep Neural Networks (DNN) form the backbone of modern Artificial Intelligence
(AI) systems. However, due to the high computational complexity and divergent shapes and …