6 citations · 10 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 4 cited
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization
Panjie Qi, Edwin Hsing-Mean Sha, Qingfeng Zhuge +5
State-of-the-art Transformer-based models, with gigantic parameters, are difficult to be accommodated on resource constrained embedded devices. Moreover, with the development of te…
cs.LG2021★ 6 cited
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices
Yuhong Song, Weiwen Jiang, Bingbing Li +6
A pruning-based AutoML framework for run-time reconfigurability, namely RT3, is proposed in this work. This enables Transformer-based large Natural Language Processing (NLP) models…