This paper presents a rescheduled dataflow of convolution and its hardware architecture
that can enhance energy efficiency. For convolution involving a large amount of …

Due to the world-wide interests on artificial intelligence, many acceleration architectures for
convolutional neural network (CNN) have been proposed recently. However, few of them …

Hardware acceleration for dilated and transposed convolution enables real time execution
of related tasks like segmentation, but current designs are specific for these convolutional …

This paper presents a low-area and energy-efficient hardware accelerator for the
convolutional neural networks (CNNs). Based on the multiply-accumulate-based …

Convolutional neural networks (CNNs) have achieved great success in image processing.
However, the heavy computational burden it imposes makes it difficult for use in embedded …

While the accuracy of convolutional neural networks (CNNs) has achieved vast
improvements by introducing larger and deeper network architectures, also the memory …

Convolutional neural network (CNN) is the state-of-the-art deep learning approach
employed in various applications. Real-time CNN implementations in resource limited …

Convolutional neural networks (CNNs) have been widely deployed in computer vision tasks.
However, the computation and resource intensive characteristics of CNN bring obstacles to …

In this brief, we introduce an architecture for accelerating convolution stages in convolutional
neural networks (CNNs) implemented in embedded vision systems. The purpose of the …

Nowadays, computer vision relies heavily on convolutional neural networks (CNNs) to
perform complex and accurate tasks. Among them, super-resolution CNNs represent a …