Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the
limited memory and computation resources. The redundancy in feature maps is an important …

Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the
limited memory and computation resources. We aim to design efficient neural networks for …

Neural network pruning is an essential approach for reducing the computational complexity
of deep models so that they can be well deployed on resource-limited devices. Compared …

Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

The dynamic neural network (DNN), in contrast to the static counterpart, offers numerous
advantages, such as improved accuracy, efficiency, and interpretability. These benefits stem …

Network pruning has attracted increasing attention recently for its capability of transferring
large-scale neural networks (eg, CNNs) into resource-constrained devices. Such a transfer …

Abstract Adder Neural Networks (ANNs) which only contain additions bring us a new way of
developing deep neural networks with low energy consumption. Unfortunately, there is an …

Abstract We proposed a Prior Gradient Mask Guided Pruning-aware Fine-Tuning (PGMPF)
framework to accelerate deep Convolutional Neural Networks (CNNs). In detail, the …

The problem of reducing processing time of large deep learning models is a fundamental
challenge in many real-world applications. Early exit methods strive towards this goal by …