Convolutional neural networks (CNN) is a specialized case of artificial neural networks
(ANN) and finds its application in computer vision and parallel distributed computing for …

Convolutional neural networks (CNNs) have dramatically improved the accuracy of image,
video, and audio processing for tasks such as object recognition, image segmentation, and …

Winograd is generally utilized to optimize convolution performance and computational
efficiency because of the reduced multiplication operations, but the reliability issues brought …

Winograd convolution is originally proposed to reduce the computing overhead by
converting multiplication in neural network (NN) with addition via linear transformation. Other …

Convolutional Neural Networks (CNNs) have been widely adopted in all kinds of artificial
intelligence applications. Most of the computational overhead of CNNs is mainly spent on …

Low-precision computation, which has been widely supported in contemporary hardware, is
considered as one of the most effective methods to accelerate convolutional neural …

Nowadays, generative adversarial network (GAN) is making continuous breakthroughs in
many machine learning tasks. The popular GANs usually involve computation-intensive …

While computer vision tasks target increasingly challenging scenarios, the need for real-time
processing of images rises as well, requiring more efficient methods to accelerate …

Deep learning on edge devices is becoming increasingly important, especially with the
explosion of IoT devices. For example, the total number of devices connected to IoT reaches …

Convolutional Neural Networks (CNNs) can benefit from the computational reductions
provided by the Winograd minimal filtering algorithm and weight pruning. However …