22 citations · 113 across the 46 of their papers we have counts for
5 papers · 1 filter
Class-Aware Pruning for Efficient Neural Networks
Mengnan Jiang, Jingcun Wang, Amro Eldebiky +4
Deep neural networks (DNNs) have demonstrated remarkable success in various fields. However, the large number of floating-point operations (FLOPs) in DNNs poses challenges for thei…
Logic Design of Neural Networks for High-Throughput and Low-Power Applications
Kangwei Xu, Grace Li Zhang, Ulf Schlichtmann +1
Neural networks (NNs) have been successfully deployed in various fields. In NNs, a large number of multiplyaccumulate (MAC) operations need to be performed. Most existing digital h…
Early-Exit with Class Exclusion for Efficient Inference of Neural Networks
Jingcun Wang, Bing Li, Grace Li Zhang
Deep neural networks (DNNs) have been successfully applied in various fields. In DNNs, a large number of multiply-accumulate (MAC) operations are required to be performed, posing c…
Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks
Chuangtao Chen, Grace Li Zhang, Xunzhao Yin +3
Deep neural networks (DNNs) have been widely deployed across diverse domains such as computer vision and natural language processing. However, the impressive accomplishments of DNN…
PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration
Richard Petri, Grace Li Zhang, Yiran Chen +2
Deep neural networks (DNNs) have been successfully applied in various fields. A major challenge of deploying DNNs, especially on edge devices, is power consumption, due to the larg…