research
∙
11/21/2022
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
Large-scale Transformer models bring significant improvements for variou...
share
research
∙
11/15/2022
HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers
While vision transformers (ViTs) have continuously achieved new mileston...
research
∙
11/02/2022
The Lottery Ticket Hypothesis for Vision Transformers
The conventional lottery ticket hypothesis (LTH) claims that there exist...
research
∙
09/22/2022
Layer Freezing Data Sieving: Missing Pieces of a Generic Framework for Sparse Training
Recently, sparse training has emerged as a promising paradigm for effici...
research
∙
10/19/2021
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization
State-of-the-art Transformer-based models, with gigantic parameters, are...
research
∙
06/16/2021
Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI
Recent research demonstrated the promise of using resistive random acces...
research
∙
09/17/2020
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
Pretrained large-scale language models have increasingly demonstrated hi...
research
∙
01/23/2020