The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Channel pruning has been broadly recognized as an effective technique to reduce the
computation and memory cost of deep convolutional neural networks. However …

The deployment constraints in practical applications necessitate the pruning of large-scale
deep learning models, ie, promoting their weight sparsity. As illustrated by the Lottery Ticket …

Structural pruning can simplify network architecture and improve inference speed. We
propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a …

Most existing neural network pruning methods hand-crafted their importance criteria and
structures to prune. This constructs heavy and unintended dependencies on heuristics and …

Neural architecture search (NAS) and network pruning are widely studied efficient AI
techniques, but not yet perfect. NAS performs exhaustive candidate architecture search …

Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …

In this article, we propose extremely low-precision vision transformers called Quantformer for
efficient inference. Conventional network quantization methods directly quantize weights …

We present a novel framework to train a large deep neural network (DNN) for only $\textit
{once} $, which can then be pruned to $\textit {any sparsity ratio} $ to preserve competitive …

Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally
on most samples, yet to produce manipulated results for inputs attached with a particular …