39 citations · 103 across the 8 of their papers we have counts for
8 papers
PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems
Runzhou Han, Mai Zheng, Suren Byna +8
Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products…
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…
A Fast Post-Training Pruning Framework for Transformers
Woosuk Kwon, Sehoon Kim, Michael W. Mahoney +3
Pruning is an effective way to reduce the huge inference cost of Transformer models. However, prior work on pruning Transformers requires retraining the models. This can add high t…
Griffin: Rethinking Sparse Optimization for Deep Learning Architectures
Jong Hoon Shin, Ali Shafiee, Ardavan Pedram +3
This paper examines the design space trade-offs of DNNs accelerators aiming to achieve competitive performance and efficiency metrics for all four combinations of dense or sparse a…
Learned Token Pruning for Transformers
Sehoon Kim, Sheng Shen, David Thorsley +4
Deploying transformer models in practice is challenging due to their inference cost, which scales quadratically with input sequence length. To address this, we present a novel Lear…
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators
Hamzah Abdel-Aziz, Ali Shafiee, Jong Hoon Shin +2
In this paper, we propose a mixed-precision convolution unit architecture which supports different integer and floating point (FP) precisions. The proposed architecture is based on…
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…