We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in low-power …

To accelerate the practical applications of artificial intelligence, this paper proposes a high
efficient layer‐wise refined pruning method for deep neural networks at the software level …

Convolutional neural networks (CNNs) have been widely employed in many applications
such as image classification, video analysis and speech recognition. Being compute …

In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …

Convolution operator is the core of convolutional neural networks (CNNs) and occupies the
most computation cost. To make CNNs more efficient, many methods have been proposed …

Filter pruning is one of the most effective ways to accelerate and compress convolutional
neural networks (CNNs). In this work, we propose a global filter pruning algorithm called …

We present a full-stack optimization framework for accelerating inference of CNNs
(Convolutional Neural Networks) and validate the approach with a field-programmable gate …

In this paper, we propose a novel progressive parameter pruning method for Convolutional
Neural Network acceleration, named Structured Probabilistic Pruning (SPP), which …

Abstract Convolutional Neural Networks (CNNs) are getting deeper and wider to improve
their performance and thus increase their computational complexity. We apply channel …

In this paper, we introduce a new channel pruning method to accelerate very deep
convolutional neural networks. Given a trained CNN model, we propose an iterative two …