39 citations · 103 across the 8 of their papers we have counts for
3 papers · 1 filter
SaiT: Sparse Vision Transformers through Adaptive Token Pruning
Ling Li, David Thorsley, Joseph Hassoun
While vision transformers have achieved impressive results, effectively and efficiently accelerating these models can further boost performances. In this work, we propose a dense/s…
MaiT: Leverage Attention Masks for More Efficient Image Transformers
Ling Li, Ali Shafiee Ardestani, Joseph Hassoun
Though image transformers have shown competitive results with convolutional neural networks in computer vision tasks, lacking inductive biases such as locality still poses problems…
Post-Training Piecewise Linear Quantization for Deep Neural Networks
Jun Fang, Ali Shafiee, Hamzah Abdel-Aziz +3
Quantization plays an important role in the energy-efficient deployment of deep neural networks on resource-limited devices. Post-training quantization is highly desirable since it…