10 citations · 15 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 5 cited
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models
Xiaofan Zhang, Zongwei Zhou, Deming Chen +1
Recently, large pre-trained models have significantly improved the performance of various Natural LanguageProcessing (NLP) tasks but they are expensive to serve due to long serving…
cs.LG2020★ 10 cited
Exploring the limits of Concurrency in ML Training on Google TPUs
Sameer Kumar, James Bradbury, Cliff Young +16
Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run.…