Neural network pruning and quantization techniques are almost as old as neural networks
themselves. However, to date, only ad-hoc comparisons between the two have been …

Background and Motivation: Coronary artery disease (CAD) has the highest mortality rate;
therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging …

Despite the remarkable success of Large Language Models (LLMs), the massive size poses
significant deployment challenges, particularly on resource-constrained hardware. While …

Embedded systems are power sensitive and have limited memory hence inferencing large
networks on such systems is difficult. Pruning techniques have been instrumental in …

Existing pruning methods utilize the importance of each weight based on specified criteria
only when searching for a sparse structure but do not utilize it during training. In this work …

Weight pruning is widely employed in compressing Deep Neural Networks (DNNs) because
of the increasing computation and storage requirement. However, related work failed to …

The adversarial robustness is critical to deep neural networks (DNNs) in deployment.
However, the improvement of adversarial robustness often requires compromising with the …

Federated learning (FL) has gained significant attention in academia and industry due to its
privacy-preserving nature. FL is a decentralized approach that allows clients to collaborate …

Most existing pruning works are resource-intensive, requiring retraining or fine-tuning of the
pruned models for accuracy. We propose a retraining-free pruning method based on …

To reduce network intrusion detection latency in a high volume of data traffic, on-device
detection with neuron pruning has been widely adopted by eliminating ineffective …