Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices

A pruning-based AutoML framework for run-time reconfigurability, namely RT 3 , is proposed in this work. This enables Transformer-based large Natural Language Processing (NLP) models to be efficiently executed on resource-constrained mobile devices and reconfigured (i.e., switching models for dynami...

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Vydané v:	2021 58th ACM/IEEE Design Automation Conference (DAC) s. 1003 - 1008
Hlavní autori:	Song, Yuhong, Jiang, Weiwen, Li, Bingbing, Qi, Panjie, Zhuge, Qingfeng, Sha, Edwin Hsing-Mean, Dasgupta, Sakyasingha, Shi, Yiyu, Ding, Caiwen
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 05.12.2021
Predmet:	Hardware Mobile handsets Reinforcement learning Software Switches Transformers Voltage
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